{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 制作数据集\n",
    "import pickle\n",
    "import json\n",
    "import random\n",
    "with open(\"all-data/train_en.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "with open('./GND/dataset/GND-Subjects-all.json') as f:\n",
    "    gnd_jsons=json.load(f)\n",
    "\n",
    "x_train=[]\n",
    "for t in test:\n",
    "    if type(t['title'])!=str:\n",
    "        t['title']=' '.join(t['title'])\n",
    "        # break\n",
    "    if type(t['abstract'])!=str:\n",
    "        t['abstract']=' '.join(t['abstract'])\n",
    "    \n",
    "\n",
    "    codes=[gnd['Code'] for gnd in t['dcterms:subject']]\n",
    "    for gnd in t['dcterms:subject']:\n",
    "        item={}\n",
    "        item['sentence2']='Classification Name is '+gnd['Classification Name']+'. Name is '+gnd['Name']\n",
    "        item['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item['label']=1\n",
    "        x_train.append(item)\n",
    "        #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_train.append(item1)\n",
    "         #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_train.append(item1)\n",
    "    \n",
    "with open(\"tmp/x_train.pkl\", \"wb\") as f:\n",
    "    pickle.dump(x_train, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 制作数据集\n",
    "import pickle\n",
    "import json\n",
    "import random\n",
    "with open(\"all-data/dev_en.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "\n",
    "x_dev=[]\n",
    "for t in test:\n",
    "    if type(t['title'])!=str:\n",
    "        t['title']=' '.join(t['title'])\n",
    "        # break\n",
    "    if type(t['abstract'])!=str:\n",
    "        t['abstract']=' '.join(t['abstract'])\n",
    "    \n",
    "\n",
    "    codes=[gnd['Code'] for gnd in t['dcterms:subject']]\n",
    "    for gnd in t['dcterms:subject']:\n",
    "        item={}\n",
    "        item['sentence2']='Classification Name is '+gnd['Classification Name']+'. Name is '+gnd['Name']\n",
    "        item['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item['label']=1\n",
    "        x_dev.append(item)\n",
    "        #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_dev.append(item1)\n",
    "    \n",
    "with open(\"tmp/x_dev.pkl\", \"wb\") as f:\n",
    "    pickle.dump(x_dev, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import json\n",
    "import random\n",
    "with open(\"tmp/x_train.pkl\", \"rb\") as f:\n",
    "    x_train = pickle.load(f)\n",
    "with open(\"tmp/x_dev.pkl\", \"rb\") as f:\n",
    "    x_dev = pickle.load(f)\n",
    "\n",
    "with open(\"all-data/train_de.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "with open('./GND/dataset/GND-Subjects-all.json') as f:\n",
    "    gnd_jsons=json.load(f)\n",
    "for t in test:\n",
    "    if type(t['title'])!=str:\n",
    "        t['title']=' '.join(t['title'])\n",
    "        # break\n",
    "    if type(t['abstract'])!=str:\n",
    "        t['abstract']=' '.join(t['abstract'])\n",
    "    \n",
    "\n",
    "    codes=[gnd['Code'] for gnd in t['dcterms:subject']]\n",
    "    for gnd in t['dcterms:subject']:\n",
    "        item={}\n",
    "        item['sentence2']='Classification Name is '+gnd['Classification Name']+'. Name is '+gnd['Name']\n",
    "        item['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item['label']=1\n",
    "        x_train.append(item)\n",
    "        #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_train.append(item1)\n",
    "\n",
    "        #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_train.append(item1)\n",
    "        \n",
    "with open(\"tmp/x_train_all.pkl\", \"wb\") as f:\n",
    "    pickle.dump(x_train, f)\n",
    "\n",
    "\n",
    "with open(\"all-data/dev_de.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "for t in test:\n",
    "    if type(t['title'])!=str:\n",
    "        t['title']=' '.join(t['title'])\n",
    "        # break\n",
    "    if type(t['abstract'])!=str:\n",
    "        t['abstract']=' '.join(t['abstract'])\n",
    "    \n",
    "\n",
    "    codes=[gnd['Code'] for gnd in t['dcterms:subject']]\n",
    "    for gnd in t['dcterms:subject']:\n",
    "        item={}\n",
    "        item['sentence2']='Classification Name is '+gnd['Classification Name']+'. Name is '+gnd['Name']\n",
    "        item['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item['label']=1\n",
    "        x_dev.append(item)\n",
    "        #从gnd_jsons中随机选举一个c，该c['Code']不在codes中\n",
    "        while True:\n",
    "            c=random.choice(gnd_jsons)\n",
    "            if c['Code'] not in codes:\n",
    "                break\n",
    "        item1={}\n",
    "        item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "        item1['sentence1']=t['title']+'.'+t['abstract']\n",
    "        item1['label']=0\n",
    "        x_dev.append(item1)\n",
    "    \n",
    "with open(\"tmp/x_dev_all.pkl\", \"wb\") as f:\n",
    "    pickle.dump(x_dev, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "697095 78136\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open(\"tmp/x_train_all.pkl\", \"rb\") as f:\n",
    "    x_train = pickle.load(f)\n",
    "with open(\"tmp/x_dev_all.pkl\", \"rb\") as f:\n",
    "    x_dev = pickle.load(f)\n",
    "\n",
    "print(len(x_train),len(x_dev))# 464730 78136\n",
    "import torch\n",
    "# 清除GPU缓存\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-06 22:45:32.764590: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1733496332.776399   47493 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1733496332.779953   47493 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-12-06 22:45:32.793848: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n",
      "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1b14b5e60fc49d793132b4d9340648b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/108925 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6994053721427917\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6943120360374451\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9517507934882768), 'eval_cosine_accuracy_threshold': np.float32(0.6994054), 'eval_cosine_f1': np.float64(0.9520351420663739), 'eval_cosine_f1_threshold': np.float32(0.69431204), 'eval_cosine_precision': 0.9444808302685274, 'eval_cosine_recall': np.float64(0.9597112726528105), 'eval_cosine_ap': np.float64(0.9890133469548481), 'eval_runtime': 85.6734, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.01}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7aca4ce670a344578e073d0ed82858ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7063482403755188\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.702224850654602\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9528258421214293), 'eval_cosine_accuracy_threshold': np.float32(0.70634824), 'eval_cosine_f1': np.float64(0.9529719054596419), 'eval_cosine_f1_threshold': np.float32(0.70222485), 'eval_cosine_precision': 0.9487530760839232, 'eval_cosine_recall': np.float64(0.9572284222381489), 'eval_cosine_ap': np.float64(0.9892589231838355), 'eval_runtime': 85.7736, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.02}\n",
      "{'loss': 0.0056, 'grad_norm': 0.09392917156219482, 'learning_rate': 4.97704737421961e-07, 'epoch': 0.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7027952671051025\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.695645809173584\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9525954745571823), 'eval_cosine_accuracy_threshold': np.float32(0.70279527), 'eval_cosine_f1': np.float64(0.9529246108764318), 'eval_cosine_f1_threshold': np.float32(0.6956458), 'eval_cosine_precision': 0.9445967356587782, 'eval_cosine_recall': np.float64(0.9614006347906214), 'eval_cosine_ap': np.float64(0.9892559253358348), 'eval_runtime': 85.8567, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.03}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7078119516372681\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7077257633209229\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9527874475273882), 'eval_cosine_accuracy_threshold': np.float32(0.70781195), 'eval_cosine_f1': np.float64(0.9531168583592807), 'eval_cosine_f1_threshold': np.float32(0.70772576), 'eval_cosine_precision': 0.9465128606406341, 'eval_cosine_recall': np.float64(0.9598136582369202), 'eval_cosine_ap': np.float64(0.989213590478012), 'eval_runtime': 86.3557, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.04}\n",
      "{'loss': 0.0058, 'grad_norm': 0.055289510637521744, 'learning_rate': 4.954094748439221e-07, 'epoch': 0.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7126851677894592\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7005090713500977\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9523779051909491), 'eval_cosine_accuracy_threshold': np.float32(0.71268517), 'eval_cosine_f1': np.float64(0.9524621887253654), 'eval_cosine_f1_threshold': np.float32(0.7005091), 'eval_cosine_precision': 0.9474924012158055, 'eval_cosine_recall': np.float64(0.9574843861984232), 'eval_cosine_ap': np.float64(0.9891887106501547), 'eval_runtime': 85.9127, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7135363817214966\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7036781311035156\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9533761646360193), 'eval_cosine_accuracy_threshold': np.float32(0.7135364), 'eval_cosine_f1': np.float64(0.9535606871590967), 'eval_cosine_f1_threshold': np.float32(0.70367813), 'eval_cosine_precision': 0.9495431472081218, 'eval_cosine_recall': np.float64(0.9576123681785604), 'eval_cosine_ap': np.float64(0.989485809443388), 'eval_runtime': 85.8845, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6845411062240601\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6845411062240601\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9530562096856763), 'eval_cosine_accuracy_threshold': np.float32(0.6845411), 'eval_cosine_f1': np.float64(0.9534482320988908), 'eval_cosine_f1_threshold': np.float32(0.6845411), 'eval_cosine_precision': 0.9455520314151941, 'eval_cosine_recall': np.float64(0.9614774239787038), 'eval_cosine_ap': np.float64(0.9894211866498297), 'eval_runtime': 85.9458, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.06}\n",
      "{'loss': 0.0059, 'grad_norm': 0.07774854451417923, 'learning_rate': 4.931142122658832e-07, 'epoch': 0.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7164800763130188\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7073328495025635\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9532353844578684), 'eval_cosine_accuracy_threshold': np.float32(0.7164801), 'eval_cosine_f1': np.float64(0.9533470110738725), 'eval_cosine_f1_threshold': np.float32(0.70733285), 'eval_cosine_precision': 0.9492703971577211, 'eval_cosine_recall': np.float64(0.9574587898023958), 'eval_cosine_ap': np.float64(0.989485052403836), 'eval_runtime': 85.8543, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7121408581733704\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6918171048164368\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9531202006757449), 'eval_cosine_accuracy_threshold': np.float32(0.71214086), 'eval_cosine_f1': np.float64(0.9532388663967611), 'eval_cosine_f1_threshold': np.float32(0.6918171), 'eval_cosine_precision': 0.9424597218052637, 'eval_cosine_recall': np.float64(0.9642674311456947), 'eval_cosine_ap': np.float64(0.9894971221958441), 'eval_runtime': 85.9688, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.08}\n",
      "{'loss': 0.0057, 'grad_norm': 0.1323438584804535, 'learning_rate': 4.908189496878443e-07, 'epoch': 0.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6958621740341187\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6948444843292236\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9535297430121839), 'eval_cosine_accuracy_threshold': np.float32(0.6958622), 'eval_cosine_f1': np.float64(0.9537689977342736), 'eval_cosine_f1_threshold': np.float32(0.6948445), 'eval_cosine_precision': 0.9486251076112827, 'eval_cosine_recall': np.float64(0.9589689771680148), 'eval_cosine_ap': np.float64(0.9896661921886816), 'eval_runtime': 86.3476, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.714860737323761\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7055243253707886\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9536961195863622), 'eval_cosine_accuracy_threshold': np.float32(0.71486074), 'eval_cosine_f1': np.float64(0.9539084278458778), 'eval_cosine_f1_threshold': np.float32(0.7055243), 'eval_cosine_precision': 0.9487757324082748, 'eval_cosine_recall': np.float64(0.959096959148152), 'eval_cosine_ap': np.float64(0.9896414857201933), 'eval_runtime': 85.9321, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7083351612091064\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7016319036483765\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9542208457049247), 'eval_cosine_accuracy_threshold': np.float32(0.70833516), 'eval_cosine_f1': np.float64(0.9543647760281303), 'eval_cosine_f1_threshold': np.float32(0.7016319), 'eval_cosine_precision': 0.9500558035714286, 'eval_cosine_recall': np.float64(0.9587130132077404), 'eval_cosine_ap': np.float64(0.9898383134720338), 'eval_runtime': 85.9443, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.11}\n",
      "{'loss': 0.0058, 'grad_norm': 0.13299928605556488, 'learning_rate': 4.885236871098053e-07, 'epoch': 0.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7117469310760498\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7114807963371277\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9539904781406778), 'eval_cosine_accuracy_threshold': np.float32(0.71174693), 'eval_cosine_f1': np.float64(0.9540569208552186), 'eval_cosine_f1_threshold': np.float32(0.7114808), 'eval_cosine_precision': 0.9526811464740563, 'eval_cosine_recall': np.float64(0.9554366745162282), 'eval_cosine_ap': np.float64(0.9897421026705779), 'eval_runtime': 85.9629, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.12}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222633361816406\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7054805755615234\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9540160745367052), 'eval_cosine_accuracy_threshold': np.float32(0.72226334), 'eval_cosine_f1': np.float64(0.9543897922427134), 'eval_cosine_f1_threshold': np.float32(0.7054806), 'eval_cosine_precision': 0.9459194448634787, 'eval_cosine_recall': np.float64(0.9630132077403502), 'eval_cosine_ap': np.float64(0.989766570357188), 'eval_runtime': 85.9184, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.13}\n",
      "{'loss': 0.0056, 'grad_norm': 0.12415090203285217, 'learning_rate': 4.862284245317664e-07, 'epoch': 0.14}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7170072793960571\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6916370391845703\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9540800655267738), 'eval_cosine_accuracy_threshold': np.float32(0.7170073), 'eval_cosine_f1': np.float64(0.9544661152007287), 'eval_cosine_f1_threshold': np.float32(0.69163704), 'eval_cosine_precision': 0.9434165124768715, 'eval_cosine_recall': np.float64(0.9657776185113136), 'eval_cosine_ap': np.float64(0.9898099576285451), 'eval_runtime': 85.8253, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.14}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6970901489257812\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6970901489257812\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9540800655267738), 'eval_cosine_accuracy_threshold': np.float32(0.69709015), 'eval_cosine_f1': np.float64(0.954445036946751), 'eval_cosine_f1_threshold': np.float32(0.69709015), 'eval_cosine_precision': 0.94691892981307, 'eval_cosine_recall': np.float64(0.9620917374833623), 'eval_cosine_ap': np.float64(0.9898732604919477), 'eval_runtime': 86.2577, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.15}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6976124048233032\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6965369582176208\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9542720384969796), 'eval_cosine_accuracy_threshold': np.float32(0.6976124), 'eval_cosine_f1': np.float64(0.9547205189276846), 'eval_cosine_f1_threshold': np.float32(0.69653696), 'eval_cosine_precision': 0.9451886413806944, 'eval_cosine_recall': np.float64(0.9644466059178868), 'eval_cosine_ap': np.float64(0.9898920252057929), 'eval_runtime': 85.8583, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.16}\n",
      "{'loss': 0.0056, 'grad_norm': 0.10991042107343674, 'learning_rate': 4.839331619537275e-07, 'epoch': 0.16}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6980373859405518\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6859644651412964\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9547071772294461), 'eval_cosine_accuracy_threshold': np.float32(0.6980374), 'eval_cosine_f1': np.float64(0.9550214244060748), 'eval_cosine_f1_threshold': np.float32(0.68596447), 'eval_cosine_precision': 0.9460742452403678, 'eval_cosine_recall': np.float64(0.9641394491655575), 'eval_cosine_ap': np.float64(0.9900115470562552), 'eval_runtime': 85.8652, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.17}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7154533267021179\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6962324380874634\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9545663970512952), 'eval_cosine_accuracy_threshold': np.float32(0.7154533), 'eval_cosine_f1': np.float64(0.9548787437472297), 'eval_cosine_f1_threshold': np.float32(0.69623244), 'eval_cosine_precision': 0.9449582675389127, 'eval_cosine_recall': np.float64(0.9650097266304904), 'eval_cosine_ap': np.float64(0.9899426258783879), 'eval_runtime': 85.8182, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.17}\n",
      "{'loss': 0.0056, 'grad_norm': 0.11264663189649582, 'learning_rate': 4.816378993756886e-07, 'epoch': 0.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.695326566696167\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6931954622268677\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9551167195658852), 'eval_cosine_accuracy_threshold': np.float32(0.69532657), 'eval_cosine_f1': np.float64(0.9554513928585039), 'eval_cosine_f1_threshold': np.float32(0.69319546), 'eval_cosine_precision': 0.9483797755642416, 'eval_cosine_recall': np.float64(0.9626292617999386), 'eval_cosine_ap': np.float64(0.9900580069362128), 'eval_runtime': 85.8264, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7058656215667725\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6990762948989868\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9551167195658852), 'eval_cosine_accuracy_threshold': np.float32(0.7058656), 'eval_cosine_f1': np.float64(0.9553845763185291), 'eval_cosine_f1_threshold': np.float32(0.6990763), 'eval_cosine_precision': 0.9478510606136947, 'eval_cosine_recall': np.float64(0.9630388041363775), 'eval_cosine_ap': np.float64(0.9900400877216382), 'eval_runtime': 85.7788, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7042144536972046\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7042144536972046\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9555518582983515), 'eval_cosine_accuracy_threshold': np.float32(0.70421445), 'eval_cosine_f1': np.float64(0.9557573981834164), 'eval_cosine_f1_threshold': np.float32(0.70421445), 'eval_cosine_precision': 0.9513580685247648, 'eval_cosine_recall': np.float64(0.9601976041773318), 'eval_cosine_ap': np.float64(0.9900938801987293), 'eval_runtime': 86.4393, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.2}\n",
      "{'loss': 0.0055, 'grad_norm': 0.16554062068462372, 'learning_rate': 4.793426367976496e-07, 'epoch': 0.21}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7057590484619141\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6929551362991333\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.955078324971844), 'eval_cosine_accuracy_threshold': np.float32(0.70575905), 'eval_cosine_f1': np.float64(0.9553014553014553), 'eval_cosine_f1_threshold': np.float32(0.69295514), 'eval_cosine_precision': 0.9463281093027929, 'eval_cosine_recall': np.float64(0.9644466059178868), 'eval_cosine_ap': np.float64(0.9901199332491069), 'eval_runtime': 85.8678, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.21}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.71608966588974\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.713484525680542\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9547071772294461), 'eval_cosine_accuracy_threshold': np.float32(0.71608967), 'eval_cosine_f1': np.float64(0.9549428037002634), 'eval_cosine_f1_threshold': np.float32(0.7134845), 'eval_cosine_precision': 0.9494699020773766, 'eval_cosine_recall': np.float64(0.9604791645336337), 'eval_cosine_ap': np.float64(0.9898505680794001), 'eval_runtime': 86.0275, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.22}\n",
      "{'loss': 0.0056, 'grad_norm': 0.14921844005584717, 'learning_rate': 4.770473742196107e-07, 'epoch': 0.23}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7179677486419678\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7044899463653564\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9551423159619126), 'eval_cosine_accuracy_threshold': np.float32(0.71796775), 'eval_cosine_f1': np.float64(0.9554007851082691), 'eval_cosine_f1_threshold': np.float32(0.70448995), 'eval_cosine_precision': 0.9454162698611599, 'eval_cosine_recall': np.float64(0.9655984437391215), 'eval_cosine_ap': np.float64(0.9900343510062297), 'eval_runtime': 85.9744, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.23}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7092281579971313\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.70915687084198\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9551423159619126), 'eval_cosine_accuracy_threshold': np.float32(0.70922816), 'eval_cosine_f1': np.float64(0.9554485020273791), 'eval_cosine_f1_threshold': np.float32(0.7091569), 'eval_cosine_precision': 0.9489710895089004, 'eval_cosine_recall': np.float64(0.96201494829528), 'eval_cosine_ap': np.float64(0.9900785245808512), 'eval_runtime': 85.9263, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.24}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7075424790382385\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.694355845451355\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9559358042387632), 'eval_cosine_accuracy_threshold': np.float32(0.7075425), 'eval_cosine_f1': np.float64(0.9560880270071325), 'eval_cosine_f1_threshold': np.float32(0.69435585), 'eval_cosine_precision': 0.9481699642551478, 'eval_cosine_recall': np.float64(0.9641394491655575), 'eval_cosine_ap': np.float64(0.9902909732675436), 'eval_runtime': 85.925, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.25}\n",
      "{'loss': 0.0056, 'grad_norm': 0.10749989002943039, 'learning_rate': 4.7475211164157173e-07, 'epoch': 0.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7122659683227539\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6982405185699463\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9550655267738303), 'eval_cosine_accuracy_threshold': np.float32(0.71226597), 'eval_cosine_f1': np.float64(0.9552677971260797), 'eval_cosine_f1_threshold': np.float32(0.6982405), 'eval_cosine_precision': 0.9467306870459288, 'eval_cosine_recall': np.float64(0.9639602743933654), 'eval_cosine_ap': np.float64(0.9902703921175904), 'eval_runtime': 86.3596, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.26}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7031156420707703\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7031156420707703\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9546303880413638), 'eval_cosine_accuracy_threshold': np.float32(0.70311564), 'eval_cosine_f1': np.float64(0.9550452084152325), 'eval_cosine_f1_threshold': np.float32(0.70311564), 'eval_cosine_precision': 0.9463922189549876, 'eval_cosine_recall': np.float64(0.9638578888092556), 'eval_cosine_ap': np.float64(0.9901438068533218), 'eval_runtime': 85.931, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.27}\n",
      "{'loss': 0.0057, 'grad_norm': 0.13059008121490479, 'learning_rate': 4.7245684906353286e-07, 'epoch': 0.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7192281484603882\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6980224847793579\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9548351592095833), 'eval_cosine_accuracy_threshold': np.float32(0.71922815), 'eval_cosine_f1': np.float64(0.9551697462366684), 'eval_cosine_f1_threshold': np.float32(0.6980225), 'eval_cosine_precision': 0.946562774915169, 'eval_cosine_recall': np.float64(0.963934677997338), 'eval_cosine_ap': np.float64(0.990270816036921), 'eval_runtime': 85.8481, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6942057609558105\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6899276971817017\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9552574997440361), 'eval_cosine_accuracy_threshold': np.float32(0.69420576), 'eval_cosine_f1': np.float64(0.9556780036734436), 'eval_cosine_f1_threshold': np.float32(0.6899277), 'eval_cosine_precision': 0.9459839004940191, 'eval_cosine_recall': np.float64(0.9655728473430941), 'eval_cosine_ap': np.float64(0.9904057087796374), 'eval_runtime': 85.8946, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6922690272331238\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6890816688537598\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9550143339817754), 'eval_cosine_accuracy_threshold': np.float32(0.692269), 'eval_cosine_f1': np.float64(0.955429990371459), 'eval_cosine_f1_threshold': np.float32(0.68908167), 'eval_cosine_precision': 0.945891029500301, 'eval_cosine_recall': np.float64(0.965163305006655), 'eval_cosine_ap': np.float64(0.9902847819066917), 'eval_runtime': 85.8811, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.29}\n",
      "{'loss': 0.0055, 'grad_norm': 0.1078212708234787, 'learning_rate': 4.7016158648549393e-07, 'epoch': 0.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7152993679046631\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7108819484710693\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.955667042080475), 'eval_cosine_accuracy_threshold': np.float32(0.71529937), 'eval_cosine_f1': np.float64(0.9558079359616586), 'eval_cosine_f1_threshold': np.float32(0.71088195), 'eval_cosine_precision': 0.9519614066268884, 'eval_cosine_recall': np.float64(0.959685676256783), 'eval_cosine_ap': np.float64(0.990364945225078), 'eval_runtime': 85.9364, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6951284408569336\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6951284408569336\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9563837411692434), 'eval_cosine_accuracy_threshold': np.float32(0.69512844), 'eval_cosine_f1': np.float64(0.9565173012146576), 'eval_cosine_f1_threshold': np.float32(0.69512844), 'eval_cosine_precision': 0.9535972321155999, 'eval_cosine_recall': np.float64(0.9594553086925361), 'eval_cosine_ap': np.float64(0.9906203856943712), 'eval_runtime': 86.3319, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.31}\n",
      "{'loss': 0.0056, 'grad_norm': 0.09845630824565887, 'learning_rate': 4.67866323907455e-07, 'epoch': 0.32}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7175225019454956\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7041436433792114\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9550271321797891), 'eval_cosine_accuracy_threshold': np.float32(0.7175225), 'eval_cosine_f1': np.float64(0.9553243728959424), 'eval_cosine_f1_threshold': np.float32(0.70414364), 'eval_cosine_precision': 0.9448255144445, 'eval_cosine_recall': np.float64(0.9660591788676154), 'eval_cosine_ap': np.float64(0.9902587750823778), 'eval_runtime': 85.9579, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.32}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6963201761245728\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6805967092514038\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9554622709122556), 'eval_cosine_accuracy_threshold': np.float32(0.6963202), 'eval_cosine_f1': np.float64(0.9558015932130639), 'eval_cosine_f1_threshold': np.float32(0.6805967), 'eval_cosine_precision': 0.94300376155245, 'eval_cosine_recall': np.float64(0.968951571618716), 'eval_cosine_ap': np.float64(0.990391189379946), 'eval_runtime': 85.8387, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.33}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7075088024139404\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7075088024139404\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9547839664175284), 'eval_cosine_accuracy_threshold': np.float32(0.7075088), 'eval_cosine_f1': np.float64(0.9551222610352493), 'eval_cosine_f1_threshold': np.float32(0.7075088), 'eval_cosine_precision': 0.9480293516907482, 'eval_cosine_recall': np.float64(0.9623221050476093), 'eval_cosine_ap': np.float64(0.99023187810463), 'eval_runtime': 85.9153, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.34}\n",
      "{'loss': 0.0053, 'grad_norm': 0.08587207645177841, 'learning_rate': 4.6557106132941603e-07, 'epoch': 0.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6955980062484741\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6955980062484741\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9558590150506808), 'eval_cosine_accuracy_threshold': np.float32(0.695598), 'eval_cosine_f1': np.float64(0.9562381840559299), 'eval_cosine_f1_threshold': np.float32(0.695598), 'eval_cosine_precision': 0.9480940998867782, 'eval_cosine_recall': np.float64(0.9645233951059691), 'eval_cosine_ap': np.float64(0.990523811357327), 'eval_runtime': 85.936, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7073119878768921\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7073119878768921\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9554750691102692), 'eval_cosine_accuracy_threshold': np.float32(0.707312), 'eval_cosine_f1': np.float64(0.9557711132864644), 'eval_cosine_f1_threshold': np.float32(0.707312), 'eval_cosine_precision': 0.9494582101992877, 'eval_cosine_recall': np.float64(0.9621685266714447), 'eval_cosine_ap': np.float64(0.9904219597377439), 'eval_runtime': 85.9099, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.36}\n",
      "{'loss': 0.0056, 'grad_norm': 0.1483040153980255, 'learning_rate': 4.632757987513771e-07, 'epoch': 0.37}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7054997086524963\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7052367925643921\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9557822258625985), 'eval_cosine_accuracy_threshold': np.float32(0.7054997), 'eval_cosine_f1': np.float64(0.9560918576130746), 'eval_cosine_f1_threshold': np.float32(0.7052368), 'eval_cosine_precision': 0.9494434488502991, 'eval_cosine_recall': np.float64(0.9628340329681581), 'eval_cosine_ap': np.float64(0.9905761034025001), 'eval_runtime': 86.3622, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.37}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7018295526504517\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7011627554893494\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.956217364595065), 'eval_cosine_accuracy_threshold': np.float32(0.70182955), 'eval_cosine_f1': np.float64(0.9565151072186702), 'eval_cosine_f1_threshold': np.float32(0.70116276), 'eval_cosine_precision': 0.9500542888165038, 'eval_cosine_recall': np.float64(0.963064400532405), 'eval_cosine_ap': np.float64(0.9906448778003328), 'eval_runtime': 85.8148, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.38}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7127519249916077\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7018689513206482\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9558846114467083), 'eval_cosine_accuracy_threshold': np.float32(0.7127519), 'eval_cosine_f1': np.float64(0.9561914408711066), 'eval_cosine_f1_threshold': np.float32(0.70186895), 'eval_cosine_precision': 0.9460813790338746, 'eval_cosine_recall': np.float64(0.9665199139961094), 'eval_cosine_ap': np.float64(0.9906406520037521), 'eval_runtime': 85.9254, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.39}\n",
      "{'loss': 0.0054, 'grad_norm': 0.19022098183631897, 'learning_rate': 4.6098053617333823e-07, 'epoch': 0.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7066628932952881\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6952725648880005\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9569084672878059), 'eval_cosine_accuracy_threshold': np.float32(0.7066629), 'eval_cosine_f1': np.float64(0.9571434013536335), 'eval_cosine_f1_threshold': np.float32(0.69527256), 'eval_cosine_precision': 0.9497265831716352, 'eval_cosine_recall': np.float64(0.9646769734821338), 'eval_cosine_ap': np.float64(0.9908503413338633), 'eval_runtime': 85.8619, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7153890132904053\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6981701850891113\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9563965393672571), 'eval_cosine_accuracy_threshold': np.float32(0.715389), 'eval_cosine_f1': np.float64(0.9566262317062374), 'eval_cosine_f1_threshold': np.float32(0.6981702), 'eval_cosine_precision': 0.9456100427617595, 'eval_cosine_recall': np.float64(0.9679021193815911), 'eval_cosine_ap': np.float64(0.9907493001486234), 'eval_runtime': 85.8517, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.4}\n",
      "{'loss': 0.0053, 'grad_norm': 0.13149985671043396, 'learning_rate': 4.586852735952993e-07, 'epoch': 0.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7128710150718689\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6930224895477295\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9560381898228729), 'eval_cosine_accuracy_threshold': np.float32(0.712871), 'eval_cosine_f1': np.float64(0.9562423307062708), 'eval_cosine_f1_threshold': np.float32(0.6930225), 'eval_cosine_precision': 0.945324028913734, 'eval_cosine_recall': np.float64(0.9674157878570697), 'eval_cosine_ap': np.float64(0.9906413661480518), 'eval_runtime': 85.8491, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7165626287460327\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7098143100738525\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9560381898228729), 'eval_cosine_accuracy_threshold': np.float32(0.7165626), 'eval_cosine_f1': np.float64(0.9562888691458308), 'eval_cosine_f1_threshold': np.float32(0.7098143), 'eval_cosine_precision': 0.9484164946377323, 'eval_cosine_recall': np.float64(0.9642930275417221), 'eval_cosine_ap': np.float64(0.9906266008756027), 'eval_runtime': 86.3347, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7134439945220947\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.707608699798584\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.955986997030818), 'eval_cosine_accuracy_threshold': np.float32(0.713444), 'eval_cosine_f1': np.float64(0.9563596713662642), 'eval_cosine_f1_threshold': np.float32(0.7076087), 'eval_cosine_precision': 0.947517837403276, 'eval_cosine_recall': np.float64(0.9653680761748745), 'eval_cosine_ap': np.float64(0.9906589310765043), 'eval_runtime': 85.896, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.43}\n",
      "{'loss': 0.0054, 'grad_norm': 0.0659937709569931, 'learning_rate': 4.5639001101726037e-07, 'epoch': 0.44}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7093798518180847\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6963874697685242\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9568316780997236), 'eval_cosine_accuracy_threshold': np.float32(0.70937985), 'eval_cosine_f1': np.float64(0.9570541237440776), 'eval_cosine_f1_threshold': np.float32(0.69638747), 'eval_cosine_precision': 0.949923094379586, 'eval_cosine_recall': np.float64(0.9642930275417221), 'eval_cosine_ap': np.float64(0.9908531481410296), 'eval_runtime': 85.9374, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.44}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7088009119033813\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6962608098983765\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9562941537831473), 'eval_cosine_accuracy_threshold': np.float32(0.7088009), 'eval_cosine_f1': np.float64(0.956599994929906), 'eval_cosine_f1_threshold': np.float32(0.6962608), 'eval_cosine_precision': 0.9474966102546075, 'eval_cosine_recall': np.float64(0.9658800040954234), 'eval_cosine_ap': np.float64(0.9907066482947853), 'eval_runtime': 85.82, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.45}\n",
      "{'loss': 0.0054, 'grad_norm': 0.22537748515605927, 'learning_rate': 4.540947484392214e-07, 'epoch': 0.46}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7004178762435913\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7004178762435913\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9566397051295178), 'eval_cosine_accuracy_threshold': np.float32(0.7004179), 'eval_cosine_f1': np.float64(0.9568912866449513), 'eval_cosine_f1_threshold': np.float32(0.7004179), 'eval_cosine_precision': 0.9513713186924401, 'eval_cosine_recall': np.float64(0.962475683423774), 'eval_cosine_ap': np.float64(0.9908258868730279), 'eval_runtime': 86.0335, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.46}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7078554034233093\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6929681301116943\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9569340636838333), 'eval_cosine_accuracy_threshold': np.float32(0.7078554), 'eval_cosine_f1': np.float64(0.9570921086018778), 'eval_cosine_f1_threshold': np.float32(0.69296813), 'eval_cosine_precision': 0.9488579190984101, 'eval_cosine_recall': np.float64(0.9654704617589843), 'eval_cosine_ap': np.float64(0.9909173572304438), 'eval_runtime': 85.9496, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7043733596801758\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6963165998458862\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9570108528719157), 'eval_cosine_accuracy_threshold': np.float32(0.70437336), 'eval_cosine_f1': np.float64(0.9572243466771689), 'eval_cosine_f1_threshold': np.float32(0.6963166), 'eval_cosine_precision': 0.9502585445831757, 'eval_cosine_recall': np.float64(0.9642930275417221), 'eval_cosine_ap': np.float64(0.9909108526889288), 'eval_runtime': 86.3507, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.48}\n",
      "{'loss': 0.0055, 'grad_norm': 0.1505746990442276, 'learning_rate': 4.517994858611825e-07, 'epoch': 0.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7175068855285645\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7149823904037476\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9565757141394492), 'eval_cosine_accuracy_threshold': np.float32(0.7175069), 'eval_cosine_f1': np.float64(0.9567853276443992), 'eval_cosine_f1_threshold': np.float32(0.7149824), 'eval_cosine_precision': 0.9521890131061931, 'eval_cosine_recall': np.float64(0.9614262311866489), 'eval_cosine_ap': np.float64(0.9907925725170031), 'eval_runtime': 85.9156, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.49}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7191022634506226\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7115218043327332\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9561533736049964), 'eval_cosine_accuracy_threshold': np.float32(0.71910226), 'eval_cosine_f1': np.float64(0.9564676616915423), 'eval_cosine_f1_threshold': np.float32(0.7115218), 'eval_cosine_precision': 0.9485701339240761, 'eval_cosine_recall': np.float64(0.9644977987099417), 'eval_cosine_ap': np.float64(0.9906978285721011), 'eval_runtime': 85.9277, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.5}\n",
      "{'loss': 0.0053, 'grad_norm': 0.10454903542995453, 'learning_rate': 4.495042232831436e-07, 'epoch': 0.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7186461091041565\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7070353627204895\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9563325483771885), 'eval_cosine_accuracy_threshold': np.float32(0.7186461), 'eval_cosine_f1': np.float64(0.956659565834855), 'eval_cosine_f1_threshold': np.float32(0.70703536), 'eval_cosine_precision': 0.9479093376218716, 'eval_cosine_recall': np.float64(0.9655728473430941), 'eval_cosine_ap': np.float64(0.9906883047406507), 'eval_runtime': 85.9065, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7028332352638245\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7022292613983154\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9572156240401352), 'eval_cosine_accuracy_threshold': np.float32(0.70283324), 'eval_cosine_f1': np.float64(0.9576207801427431), 'eval_cosine_f1_threshold': np.float32(0.70222926), 'eval_cosine_precision': 0.9486374481979154, 'eval_cosine_recall': np.float64(0.9667758779563838), 'eval_cosine_ap': np.float64(0.9908645537342826), 'eval_runtime': 85.8609, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7079026103019714\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6945215463638306\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.957586771782533), 'eval_cosine_accuracy_threshold': np.float32(0.7079026), 'eval_cosine_f1': np.float64(0.9578976682953998), 'eval_cosine_f1_threshold': np.float32(0.69452155), 'eval_cosine_precision': 0.9475582268970699, 'eval_cosine_recall': np.float64(0.9684652400941948), 'eval_cosine_ap': np.float64(0.9910442479925725), 'eval_runtime': 85.9041, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.52}\n",
      "{'loss': 0.0055, 'grad_norm': 0.09263815730810165, 'learning_rate': 4.4720896070510467e-07, 'epoch': 0.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7142236828804016\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7005091309547424\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9575355789904781), 'eval_cosine_accuracy_threshold': np.float32(0.7142237), 'eval_cosine_f1': np.float64(0.9577496925982735), 'eval_cosine_f1_threshold': np.float32(0.70050913), 'eval_cosine_precision': 0.9487179487179487, 'eval_cosine_recall': np.float64(0.9669550527285758), 'eval_cosine_ap': np.float64(0.9910345845088312), 'eval_runtime': 86.2825, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7124763131141663\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6981750130653381\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9571644312480803), 'eval_cosine_accuracy_threshold': np.float32(0.7124763), 'eval_cosine_f1': np.float64(0.9574246617075832), 'eval_cosine_f1_threshold': np.float32(0.698175), 'eval_cosine_precision': 0.9496852178292622, 'eval_cosine_recall': np.float64(0.9652912869867922), 'eval_cosine_ap': np.float64(0.9909163471324104), 'eval_runtime': 85.8624, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.54}\n",
      "{'loss': 0.0053, 'grad_norm': 0.1363401561975479, 'learning_rate': 4.449136981270657e-07, 'epoch': 0.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7102723121643066\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7102723121643066\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9577147537626702), 'eval_cosine_accuracy_threshold': np.float32(0.7102723), 'eval_cosine_f1': np.float64(0.9579194049620459), 'eval_cosine_f1_threshold': np.float32(0.7102723), 'eval_cosine_precision': 0.9533056175218009, 'eval_cosine_recall': np.float64(0.9625780690078837), 'eval_cosine_ap': np.float64(0.9909797590147007), 'eval_runtime': 85.8285, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7236411571502686\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7236411571502686\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9579195249308897), 'eval_cosine_accuracy_threshold': np.float32(0.72364116), 'eval_cosine_f1': np.float64(0.9580665731411809), 'eval_cosine_f1_threshold': np.float32(0.72364116), 'eval_cosine_precision': 0.9547303136597021, 'eval_cosine_recall': np.float64(0.9614262311866489), 'eval_cosine_ap': np.float64(0.9910111498259766), 'eval_runtime': 85.8595, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.706822395324707\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.706822395324707\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9576379645745879), 'eval_cosine_accuracy_threshold': np.float32(0.7068224), 'eval_cosine_f1': np.float64(0.9578590889415121), 'eval_cosine_f1_threshold': np.float32(0.7068224), 'eval_cosine_precision': 0.9528851512234663, 'eval_cosine_recall': np.float64(0.962885225760213), 'eval_cosine_ap': np.float64(0.9911144695774358), 'eval_runtime': 85.7599, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.57}\n",
      "{'loss': 0.0054, 'grad_norm': 0.07060351967811584, 'learning_rate': 4.4261843554902676e-07, 'epoch': 0.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7018163800239563\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6983929872512817\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9580475069110269), 'eval_cosine_accuracy_threshold': np.float32(0.7018164), 'eval_cosine_f1': np.float64(0.9582532306321218), 'eval_cosine_f1_threshold': np.float32(0.698393), 'eval_cosine_precision': 0.9532892570357423, 'eval_cosine_recall': np.float64(0.9632691717006245), 'eval_cosine_ap': np.float64(0.9911975150544742), 'eval_runtime': 85.9274, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.58}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.715589165687561\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7052836418151855\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9574715880004095), 'eval_cosine_accuracy_threshold': np.float32(0.71558917), 'eval_cosine_f1': np.float64(0.9577346591053582), 'eval_cosine_f1_threshold': np.float32(0.70528364), 'eval_cosine_precision': 0.9515640002021325, 'eval_cosine_recall': np.float64(0.9639858707893929), 'eval_cosine_ap': np.float64(0.9911081706693817), 'eval_runtime': 86.2324, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.59}\n",
      "{'loss': 0.0052, 'grad_norm': 0.12681427597999573, 'learning_rate': 4.403231729709879e-07, 'epoch': 0.6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7209163904190063\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7165364027023315\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9575227807924644), 'eval_cosine_accuracy_threshold': np.float32(0.7209164), 'eval_cosine_f1': np.float64(0.95772854596384), 'eval_cosine_f1_threshold': np.float32(0.7165364), 'eval_cosine_precision': 0.952827320632347, 'eval_cosine_recall': np.float64(0.9626804545919935), 'eval_cosine_ap': np.float64(0.9910051424503296), 'eval_runtime': 85.982, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7209450006484985\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7028892040252686\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9577403501586976), 'eval_cosine_accuracy_threshold': np.float32(0.720945), 'eval_cosine_f1': np.float64(0.9578290325036157), 'eval_cosine_f1_threshold': np.float32(0.7028892), 'eval_cosine_precision': 0.9495648236655431, 'eval_cosine_recall': np.float64(0.9662383536398075), 'eval_cosine_ap': np.float64(0.9910129563565957), 'eval_runtime': 85.8808, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.61}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7266204357147217\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.700497031211853\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.957177229446094), 'eval_cosine_accuracy_threshold': np.float32(0.72662044), 'eval_cosine_f1': np.float64(0.9574581161107456), 'eval_cosine_f1_threshold': np.float32(0.70049703), 'eval_cosine_precision': 0.9467717717717717, 'eval_cosine_recall': np.float64(0.9683884509061125), 'eval_cosine_ap': np.float64(0.9908920120804754), 'eval_runtime': 85.9066, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.62}\n",
      "{'loss': 0.0052, 'grad_norm': 0.16373413801193237, 'learning_rate': 4.3802791039294896e-07, 'epoch': 0.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7273349165916443\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7153579592704773\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9572540186341764), 'eval_cosine_accuracy_threshold': np.float32(0.7273349), 'eval_cosine_f1': np.float64(0.9574554707379135), 'eval_cosine_f1_threshold': np.float32(0.71535796), 'eval_cosine_precision': 0.9518364868966913, 'eval_cosine_recall': np.float64(0.9631411897204873), 'eval_cosine_ap': np.float64(0.9908436055149983), 'eval_runtime': 85.8168, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7195549011230469\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7140803337097168\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9568956690897922), 'eval_cosine_accuracy_threshold': np.float32(0.7195549), 'eval_cosine_f1': np.float64(0.9570142946245592), 'eval_cosine_f1_threshold': np.float32(0.71408033), 'eval_cosine_precision': 0.9518648874484086, 'eval_cosine_recall': np.float64(0.9622197194634995), 'eval_cosine_ap': np.float64(0.9907378325620556), 'eval_runtime': 85.7966, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.63}\n",
      "{'loss': 0.0051, 'grad_norm': 0.16388681530952454, 'learning_rate': 4.3573264781491e-07, 'epoch': 0.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7187071442604065\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7148487567901611\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9566141087334903), 'eval_cosine_accuracy_threshold': np.float32(0.71870714), 'eval_cosine_f1': np.float64(0.9568920585281517), 'eval_cosine_f1_threshold': np.float32(0.71484876), 'eval_cosine_precision': 0.9505240560676853, 'eval_cosine_recall': np.float64(0.9633459608887068), 'eval_cosine_ap': np.float64(0.9907551433984546), 'eval_runtime': 86.2258, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7060551643371582\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7059704065322876\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9573436060202724), 'eval_cosine_accuracy_threshold': np.float32(0.70605516), 'eval_cosine_f1': np.float64(0.9575862463891681), 'eval_cosine_f1_threshold': np.float32(0.7059704), 'eval_cosine_precision': 0.9521700620017715, 'eval_cosine_recall': np.float64(0.963064400532405), 'eval_cosine_ap': np.float64(0.9909774200965475), 'eval_runtime': 85.8551, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7066141963005066\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7058440446853638\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.956857274495751), 'eval_cosine_accuracy_threshold': np.float32(0.7066142), 'eval_cosine_f1': np.float64(0.957160467155511), 'eval_cosine_f1_threshold': np.float32(0.70584404), 'eval_cosine_precision': 0.9504808056333762, 'eval_cosine_recall': np.float64(0.963934677997338), 'eval_cosine_ap': np.float64(0.9909383511181955), 'eval_runtime': 85.9379, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.66}\n",
      "{'loss': 0.005, 'grad_norm': 0.11576761305332184, 'learning_rate': 4.3343738523687106e-07, 'epoch': 0.67}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7128155827522278\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7127938270568848\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9576123681785604), 'eval_cosine_accuracy_threshold': np.float32(0.7128156), 'eval_cosine_f1': np.float64(0.9578668837777324), 'eval_cosine_f1_threshold': np.float32(0.7127938), 'eval_cosine_precision': 0.9521497218007081, 'eval_cosine_recall': np.float64(0.9636531176410361), 'eval_cosine_ap': np.float64(0.9911092095824028), 'eval_runtime': 85.8395, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.67}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.716485857963562\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7130807638168335\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9580219105149995), 'eval_cosine_accuracy_threshold': np.float32(0.71648586), 'eval_cosine_f1': np.float64(0.9582464527284749), 'eval_cosine_f1_threshold': np.float32(0.71308076), 'eval_cosine_precision': 0.9520008084074374, 'eval_cosine_recall': np.float64(0.964574587898024), 'eval_cosine_ap': np.float64(0.9911513091550024), 'eval_runtime': 85.9254, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.68}\n",
      "{'loss': 0.0053, 'grad_norm': 0.1421668380498886, 'learning_rate': 4.3114212265883213e-07, 'epoch': 0.69}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7202310562133789\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7089188098907471\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9580347087130132), 'eval_cosine_accuracy_threshold': np.float32(0.72023106), 'eval_cosine_f1': np.float64(0.9582661878719436), 'eval_cosine_f1_threshold': np.float32(0.7089188), 'eval_cosine_precision': 0.950152235726328, 'eval_cosine_recall': np.float64(0.9665199139961094), 'eval_cosine_ap': np.float64(0.9911899333287282), 'eval_runtime': 86.3845, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.69}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7107739448547363\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7100873589515686\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9578427357428074), 'eval_cosine_accuracy_threshold': np.float32(0.71077394), 'eval_cosine_f1': np.float64(0.9580399098399276), 'eval_cosine_f1_threshold': np.float32(0.71008736), 'eval_cosine_precision': 0.9532932917712056, 'eval_cosine_recall': np.float64(0.9628340329681581), 'eval_cosine_ap': np.float64(0.9911900479547444), 'eval_runtime': 85.9262, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.720954179763794\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7209120988845825\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9571388348520529), 'eval_cosine_accuracy_threshold': np.float32(0.7209542), 'eval_cosine_f1': np.float64(0.9572989582934884), 'eval_cosine_f1_threshold': np.float32(0.7209121), 'eval_cosine_precision': 0.9537359315058053, 'eval_cosine_recall': np.float64(0.9608887068700727), 'eval_cosine_ap': np.float64(0.9909514523097567), 'eval_runtime': 85.8957, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.71}\n",
      "{'loss': 0.0051, 'grad_norm': 0.06008654832839966, 'learning_rate': 4.2884686008079326e-07, 'epoch': 0.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7151057124137878\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7014669179916382\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.957356404218286), 'eval_cosine_accuracy_threshold': np.float32(0.7151057), 'eval_cosine_f1': np.float64(0.9576705516115562), 'eval_cosine_f1_threshold': np.float32(0.7014669), 'eval_cosine_precision': 0.9470404204730322, 'eval_cosine_recall': np.float64(0.9685420292822771), 'eval_cosine_ap': np.float64(0.9910848496157189), 'eval_runtime': 85.9251, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7158221006393433\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7158149480819702\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9572412204361626), 'eval_cosine_accuracy_threshold': np.float32(0.7158221), 'eval_cosine_f1': np.float64(0.9575449520299892), 'eval_cosine_f1_threshold': np.float32(0.71581495), 'eval_cosine_precision': 0.9507911272617156, 'eval_cosine_recall': np.float64(0.9643954131258319), 'eval_cosine_ap': np.float64(0.9909748489023275), 'eval_runtime': 85.8187, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.73}\n",
      "{'loss': 0.0053, 'grad_norm': 0.08709973096847534, 'learning_rate': 4.2655159750275433e-07, 'epoch': 0.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7187522649765015\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7113697528839111\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9575739735845193), 'eval_cosine_accuracy_threshold': np.float32(0.71875226), 'eval_cosine_f1': np.float64(0.9579778670756905), 'eval_cosine_f1_threshold': np.float32(0.71136975), 'eval_cosine_precision': 0.9489439714709058, 'eval_cosine_recall': np.float64(0.9671854202928227), 'eval_cosine_ap': np.float64(0.9910713906608666), 'eval_runtime': 85.8186, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7047133445739746\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7047133445739746\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9575483771884918), 'eval_cosine_accuracy_threshold': np.float32(0.70471334), 'eval_cosine_f1': np.float64(0.9579034202677834), 'eval_cosine_f1_threshold': np.float32(0.70471334), 'eval_cosine_precision': 0.9499584665340952, 'eval_cosine_recall': np.float64(0.9659823896795331), 'eval_cosine_ap': np.float64(0.9911225127173809), 'eval_runtime': 86.2916, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7077360153198242\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7014340758323669\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9590201699600697), 'eval_cosine_accuracy_threshold': np.float32(0.707736), 'eval_cosine_f1': np.float64(0.9592603880930611), 'eval_cosine_f1_threshold': np.float32(0.7014341), 'eval_cosine_precision': 0.9519104748462547, 'eval_cosine_recall': np.float64(0.9667246851643289), 'eval_cosine_ap': np.float64(0.9914331892822357), 'eval_runtime': 85.7679, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.75}\n",
      "{'loss': 0.0052, 'grad_norm': 0.07386606931686401, 'learning_rate': 4.2425633492471535e-07, 'epoch': 0.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7195807695388794\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7051132917404175\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9577915429507525), 'eval_cosine_accuracy_threshold': np.float32(0.71958077), 'eval_cosine_f1': np.float64(0.9579512157996879), 'eval_cosine_f1_threshold': np.float32(0.7051133), 'eval_cosine_precision': 0.9495084110739521, 'eval_cosine_recall': np.float64(0.9665455103921368), 'eval_cosine_ap': np.float64(0.9911821873091007), 'eval_runtime': 85.7197, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7171589136123657\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7124879360198975\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9583546636633562), 'eval_cosine_accuracy_threshold': np.float32(0.7171589), 'eval_cosine_f1': np.float64(0.9585307453257935), 'eval_cosine_f1_threshold': np.float32(0.71248794), 'eval_cosine_precision': 0.9539140133847089, 'eval_cosine_recall': np.float64(0.9631923825125422), 'eval_cosine_ap': np.float64(0.9913596432871982), 'eval_runtime': 85.7571, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.77}\n",
      "{'loss': 0.0053, 'grad_norm': 0.06894229352474213, 'learning_rate': 4.219610723466764e-07, 'epoch': 0.78}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7070033550262451\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7070033550262451\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9585082420395208), 'eval_cosine_accuracy_threshold': np.float32(0.70700336), 'eval_cosine_f1': np.float64(0.958778354185739), 'eval_cosine_f1_threshold': np.float32(0.70700336), 'eval_cosine_precision': 0.9525770591207681, 'eval_cosine_recall': np.float64(0.9650609194225453), 'eval_cosine_ap': np.float64(0.9913802327840546), 'eval_runtime': 85.8336, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.78}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7194174528121948\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7125544548034668\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9582394798812327), 'eval_cosine_accuracy_threshold': np.float32(0.71941745), 'eval_cosine_f1': np.float64(0.9584842362090655), 'eval_cosine_f1_threshold': np.float32(0.71255445), 'eval_cosine_precision': 0.9517476340694007, 'eval_cosine_recall': np.float64(0.9653168833828197), 'eval_cosine_ap': np.float64(0.9912772373116377), 'eval_runtime': 85.8234, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7125211954116821\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7125211954116821\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9582778744752739), 'eval_cosine_accuracy_threshold': np.float32(0.7125212), 'eval_cosine_f1': np.float64(0.9585051677613156), 'eval_cosine_f1_threshold': np.float32(0.7125212), 'eval_cosine_precision': 0.9533117277699007, 'eval_cosine_recall': np.float64(0.9637555032251459), 'eval_cosine_ap': np.float64(0.9913097659401943), 'eval_runtime': 86.3204, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.8}\n",
      "{'loss': 0.0052, 'grad_norm': 0.12381961196660995, 'learning_rate': 4.1966580976863755e-07, 'epoch': 0.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.71515953540802\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7137234807014465\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9589433807719873), 'eval_cosine_accuracy_threshold': np.float32(0.71515954), 'eval_cosine_f1': np.float64(0.9590811747909443), 'eval_cosine_f1_threshold': np.float32(0.7137235), 'eval_cosine_precision': 0.9552818689690198, 'eval_cosine_recall': np.float64(0.9629108221562404), 'eval_cosine_ap': np.float64(0.9914400969628938), 'eval_runtime': 85.7794, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7166157960891724\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7165790796279907\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9585210402375346), 'eval_cosine_accuracy_threshold': np.float32(0.7166158), 'eval_cosine_f1': np.float64(0.9586538584204014), 'eval_cosine_f1_threshold': np.float32(0.7165791), 'eval_cosine_precision': 0.9555939876395636, 'eval_cosine_recall': np.float64(0.9617333879389782), 'eval_cosine_ap': np.float64(0.9913238794469532), 'eval_runtime': 85.917, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.82}\n",
      "{'loss': 0.0052, 'grad_norm': 0.10400302708148956, 'learning_rate': 4.173705471905986e-07, 'epoch': 0.83}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.715796947479248\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7136882543563843\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9593145285143851), 'eval_cosine_accuracy_threshold': np.float32(0.71579695), 'eval_cosine_f1': np.float64(0.9594472278527811), 'eval_cosine_f1_threshold': np.float32(0.71368825), 'eval_cosine_precision': 0.9557310847535113, 'eval_cosine_recall': np.float64(0.9631923825125422), 'eval_cosine_ap': np.float64(0.9915464020556912), 'eval_runtime': 85.8404, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.83}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7185847759246826\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7064162492752075\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.958866591583905), 'eval_cosine_accuracy_threshold': np.float32(0.7185848), 'eval_cosine_f1': np.float64(0.9591075236064576), 'eval_cosine_f1_threshold': np.float32(0.70641625), 'eval_cosine_precision': 0.9511881985701339, 'eval_cosine_recall': np.float64(0.9671598238967953), 'eval_cosine_ap': np.float64(0.9914596104633021), 'eval_runtime': 85.9027, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7133204936981201\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7117961049079895\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9588921879799325), 'eval_cosine_accuracy_threshold': np.float32(0.7133205), 'eval_cosine_f1': np.float64(0.9590963502534193), 'eval_cosine_f1_threshold': np.float32(0.7117961), 'eval_cosine_precision': 0.954356530994982, 'eval_cosine_recall': np.float64(0.9638834852052831), 'eval_cosine_ap': np.float64(0.9914226239684866), 'eval_runtime': 85.8723, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.84}\n",
      "{'loss': 0.0049, 'grad_norm': 0.04924355819821358, 'learning_rate': 4.1507528461255964e-07, 'epoch': 0.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7151358127593994\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7070026397705078\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9586874168117129), 'eval_cosine_accuracy_threshold': np.float32(0.7151358), 'eval_cosine_f1': np.float64(0.9590116057597075), 'eval_cosine_f1_threshold': np.float32(0.70700264), 'eval_cosine_precision': 0.9515446253086731, 'eval_cosine_recall': np.float64(0.9665967031841917), 'eval_cosine_ap': np.float64(0.9914545041463336), 'eval_runtime': 86.2794, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7171338796615601\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.699175238609314\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9587642059997953), 'eval_cosine_accuracy_threshold': np.float32(0.7171339), 'eval_cosine_f1': np.float64(0.9590932744418638), 'eval_cosine_f1_threshold': np.float32(0.69917524), 'eval_cosine_precision': 0.9512096875708064, 'eval_cosine_recall': np.float64(0.9671086311047404), 'eval_cosine_ap': np.float64(0.9915714740657057), 'eval_runtime': 85.8515, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.86}\n",
      "{'loss': 0.0051, 'grad_norm': 0.07826285809278488, 'learning_rate': 4.127800220345207e-07, 'epoch': 0.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7275763750076294\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7143869400024414\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9587258114057541), 'eval_cosine_accuracy_threshold': np.float32(0.7275764), 'eval_cosine_f1': np.float64(0.9590055809233892), 'eval_cosine_f1_threshold': np.float32(0.71438694), 'eval_cosine_precision': 0.9505179523282712, 'eval_cosine_recall': np.float64(0.9676461554213167), 'eval_cosine_ap': np.float64(0.9914475089535197), 'eval_runtime': 85.9106, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.6953396797180176\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6953396797180176\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9596472816627419), 'eval_cosine_accuracy_threshold': np.float32(0.6953397), 'eval_cosine_f1': np.float64(0.9598492276738531), 'eval_cosine_f1_threshold': np.float32(0.6953397), 'eval_cosine_precision': 0.9550695623527027, 'eval_cosine_recall': np.float64(0.9646769734821338), 'eval_cosine_ap': np.float64(0.9917121381161534), 'eval_runtime': 85.8986, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7083041667938232\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7005500793457031\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9590073717620559), 'eval_cosine_accuracy_threshold': np.float32(0.70830417), 'eval_cosine_f1': np.float64(0.9592656921536061), 'eval_cosine_f1_threshold': np.float32(0.7005501), 'eval_cosine_precision': 0.9503378632972443, 'eval_cosine_recall': np.float64(0.968362854510085), 'eval_cosine_ap': np.float64(0.9915459863835936), 'eval_runtime': 85.8795, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.89}\n",
      "{'loss': 0.0051, 'grad_norm': 0.12494246661663055, 'learning_rate': 4.104847594564818e-07, 'epoch': 0.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7052429914474487\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7052429914474487\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.959096959148152), 'eval_cosine_accuracy_threshold': np.float32(0.705243), 'eval_cosine_f1': np.float64(0.9593777009507347), 'eval_cosine_f1_threshold': np.float32(0.705243), 'eval_cosine_precision': 0.9528378105433246, 'eval_cosine_recall': np.float64(0.9660079860755606), 'eval_cosine_ap': np.float64(0.991565586028732), 'eval_runtime': 85.9529, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7080972790718079\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7080972790718079\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9589049861779462), 'eval_cosine_accuracy_threshold': np.float32(0.7080973), 'eval_cosine_f1': np.float64(0.959092935855787), 'eval_cosine_f1_threshold': np.float32(0.7080973), 'eval_cosine_precision': 0.954726456489208, 'eval_cosine_recall': np.float64(0.9634995392648715), 'eval_cosine_ap': np.float64(0.9915042243917155), 'eval_runtime': 85.8187, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.91}\n",
      "{'loss': 0.0049, 'grad_norm': 0.0426471009850502, 'learning_rate': 4.081894968784429e-07, 'epoch': 0.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7109496593475342\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7109496593475342\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9585338384355483), 'eval_cosine_accuracy_threshold': np.float32(0.71094966), 'eval_cosine_f1': np.float64(0.9588132102814431), 'eval_cosine_f1_threshold': np.float32(0.71094966), 'eval_cosine_precision': 0.9523965856861457, 'eval_cosine_recall': np.float64(0.9653168833828197), 'eval_cosine_ap': np.float64(0.9914563577208338), 'eval_runtime': 86.2904, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7196191549301147\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7072911262512207\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9585594348315757), 'eval_cosine_accuracy_threshold': np.float32(0.71961915), 'eval_cosine_f1': np.float64(0.9589662380439602), 'eval_cosine_f1_threshold': np.float32(0.7072911), 'eval_cosine_precision': 0.9493566107306796, 'eval_cosine_recall': np.float64(0.968772396846524), 'eval_cosine_ap': np.float64(0.9914423095896988), 'eval_runtime': 85.9209, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7238497734069824\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7238497734069824\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9589817753660285), 'eval_cosine_accuracy_threshold': np.float32(0.7238498), 'eval_cosine_f1': np.float64(0.95928762877431), 'eval_cosine_f1_threshold': np.float32(0.7238498), 'eval_cosine_precision': 0.9521876182070357, 'eval_cosine_recall': np.float64(0.9664943176000819), 'eval_cosine_ap': np.float64(0.9914187213103666), 'eval_runtime': 85.9907, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.94}\n",
      "{'loss': 0.0051, 'grad_norm': 0.2066899538040161, 'learning_rate': 4.0589423430040394e-07, 'epoch': 0.94}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7137712240219116\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7026664614677429\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9595832906726733), 'eval_cosine_accuracy_threshold': np.float32(0.7137712), 'eval_cosine_f1': np.float64(0.959731884425938), 'eval_cosine_f1_threshold': np.float32(0.70266646), 'eval_cosine_precision': 0.9520451339915373, 'eval_cosine_recall': np.float64(0.9675437698372069), 'eval_cosine_ap': np.float64(0.991602322056849), 'eval_runtime': 85.9142, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.705713152885437\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6903420686721802\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9594937032865772), 'eval_cosine_accuracy_threshold': np.float32(0.70571315), 'eval_cosine_f1': np.float64(0.9598266335479293), 'eval_cosine_f1_threshold': np.float32(0.69034207), 'eval_cosine_precision': 0.9505271084337349, 'eval_cosine_recall': np.float64(0.9693099211631002), 'eval_cosine_ap': np.float64(0.9916371236639705), 'eval_runtime': 85.9264, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.95}\n",
      "{'loss': 0.005, 'grad_norm': 0.1347266435623169, 'learning_rate': 4.03598971722365e-07, 'epoch': 0.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7141050100326538\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7079261541366577\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9589177843759599), 'eval_cosine_accuracy_threshold': np.float32(0.714105), 'eval_cosine_f1': np.float64(0.9593242661398576), 'eval_cosine_f1_threshold': np.float32(0.70792615), 'eval_cosine_precision': 0.9493458318712718, 'eval_cosine_recall': np.float64(0.9695146923313197), 'eval_cosine_ap': np.float64(0.9914392393465848), 'eval_runtime': 86.3266, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.706005334854126\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7058460712432861\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9600824203952083), 'eval_cosine_accuracy_threshold': np.float32(0.70600533), 'eval_cosine_f1': np.float64(0.9604072254592076), 'eval_cosine_f1_threshold': np.float32(0.7058461), 'eval_cosine_precision': 0.9526555692664131, 'eval_cosine_recall': np.float64(0.9682860653220027), 'eval_cosine_ap': np.float64(0.9917078164047478), 'eval_runtime': 85.8717, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7189124822616577\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7189022302627563\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9595065014845909), 'eval_cosine_accuracy_threshold': np.float32(0.7189125), 'eval_cosine_f1': np.float64(0.9597004279600571), 'eval_cosine_f1_threshold': np.float32(0.71890223), 'eval_cosine_precision': 0.9551262549437177, 'eval_cosine_recall': np.float64(0.9643186239377496), 'eval_cosine_ap': np.float64(0.9916076922173399), 'eval_runtime': 85.8879, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.98}\n",
      "{'loss': 0.0051, 'grad_norm': 0.14014676213264465, 'learning_rate': 4.013037091443261e-07, 'epoch': 0.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7155200242996216\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7153505086898804\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9587258114057541), 'eval_cosine_accuracy_threshold': np.float32(0.71552), 'eval_cosine_f1': np.float64(0.9590179558537608), 'eval_cosine_f1_threshold': np.float32(0.7153505), 'eval_cosine_precision': 0.9522776025236593, 'eval_cosine_recall': np.float64(0.9658544076993959), 'eval_cosine_ap': np.float64(0.9914109946338807), 'eval_runtime': 85.8888, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 0.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7091994285583496\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7091994285583496\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9594041159004812), 'eval_cosine_accuracy_threshold': np.float32(0.7091994), 'eval_cosine_f1': np.float64(0.9596735233542679), 'eval_cosine_f1_threshold': np.float32(0.7091994), 'eval_cosine_precision': 0.953346804748674, 'eval_cosine_recall': np.float64(0.9660847752636429), 'eval_cosine_ap': np.float64(0.991690250024174), 'eval_runtime': 86.4203, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.0}\n",
      "{'loss': 0.0049, 'grad_norm': 0.11439488083124161, 'learning_rate': 3.9900844656628716e-07, 'epoch': 1.01}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7160828709602356\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7107498645782471\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9584186546534248), 'eval_cosine_accuracy_threshold': np.float32(0.7160829), 'eval_cosine_f1': np.float64(0.9586846221340556), 'eval_cosine_f1_threshold': np.float32(0.71074986), 'eval_cosine_precision': 0.9525915443127543, 'eval_cosine_recall': np.float64(0.9648561482543258), 'eval_cosine_ap': np.float64(0.9914069268340582), 'eval_runtime': 85.9472, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.01}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7177732586860657\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7068520784378052\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9593017303163714), 'eval_cosine_accuracy_threshold': np.float32(0.71777326), 'eval_cosine_f1': np.float64(0.9595885191770385), 'eval_cosine_f1_threshold': np.float32(0.7068521), 'eval_cosine_precision': 0.9522837265577737, 'eval_cosine_recall': np.float64(0.9670062455206307), 'eval_cosine_ap': np.float64(0.9916008129838265), 'eval_runtime': 85.8512, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7123250961303711\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.709092915058136\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.95936572130644), 'eval_cosine_accuracy_threshold': np.float32(0.7123251), 'eval_cosine_f1': np.float64(0.9596412556053812), 'eval_cosine_f1_threshold': np.float32(0.7090929), 'eval_cosine_precision': 0.9525863156036418, 'eval_cosine_recall': np.float64(0.9668014743524111), 'eval_cosine_ap': np.float64(0.9915804525140578), 'eval_runtime': 85.7517, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.03}\n",
      "{'loss': 0.0048, 'grad_norm': 0.08315358310937881, 'learning_rate': 3.9671318398824823e-07, 'epoch': 1.03}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.718285322189331\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715330958366394\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9592889321183578), 'eval_cosine_accuracy_threshold': np.float32(0.7182853), 'eval_cosine_f1': np.float64(0.959494186194438), 'eval_cosine_f1_threshold': np.float32(0.71533096), 'eval_cosine_precision': 0.9537912893924831, 'eval_cosine_recall': np.float64(0.9652656905907648), 'eval_cosine_ap': np.float64(0.9915427887518343), 'eval_runtime': 85.8479, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.04}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7089568376541138\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7089568376541138\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9590073717620559), 'eval_cosine_accuracy_threshold': np.float32(0.70895684), 'eval_cosine_f1': np.float64(0.9592706094785163), 'eval_cosine_f1_threshold': np.float32(0.70895684), 'eval_cosine_precision': 0.9531498749147146, 'eval_cosine_recall': np.float64(0.9654704617589843), 'eval_cosine_ap': np.float64(0.9915465349447279), 'eval_runtime': 85.8615, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.05}\n",
      "{'loss': 0.0051, 'grad_norm': 0.10295895487070084, 'learning_rate': 3.944179214102093e-07, 'epoch': 1.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7105940580368042\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7105940580368042\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9599928330091123), 'eval_cosine_accuracy_threshold': np.float32(0.71059406), 'eval_cosine_f1': np.float64(0.9602178726870116), 'eval_cosine_f1_threshold': np.float32(0.71059406), 'eval_cosine_precision': 0.954846874209061, 'eval_cosine_recall': np.float64(0.9656496365311764), 'eval_cosine_ap': np.float64(0.9917385078140852), 'eval_runtime': 86.2577, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7044634819030762\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.69758540391922\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9598904474250025), 'eval_cosine_accuracy_threshold': np.float32(0.7044635), 'eval_cosine_f1': np.float64(0.9600915681037772), 'eval_cosine_f1_threshold': np.float32(0.6975854), 'eval_cosine_precision': 0.954097366159446, 'eval_cosine_recall': np.float64(0.9661615644517252), 'eval_cosine_ap': np.float64(0.9917483241772351), 'eval_runtime': 85.8332, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7125551700592041\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7107610702514648\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9596088870687007), 'eval_cosine_accuracy_threshold': np.float32(0.71255517), 'eval_cosine_f1': np.float64(0.9598002751311968), 'eval_cosine_f1_threshold': np.float32(0.7107611), 'eval_cosine_precision': 0.9552738336713996, 'eval_cosine_recall': np.float64(0.9643698167298045), 'eval_cosine_ap': np.float64(0.9917294778946987), 'eval_runtime': 85.9083, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.07}\n",
      "{'loss': 0.0049, 'grad_norm': 0.10320186614990234, 'learning_rate': 3.921226588321704e-07, 'epoch': 1.08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7041126489639282\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7035953998565674\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9600696221971946), 'eval_cosine_accuracy_threshold': np.float32(0.70411265), 'eval_cosine_f1': np.float64(0.9602790650303), 'eval_cosine_f1_threshold': np.float32(0.7035954), 'eval_cosine_precision': 0.9552684903748734, 'eval_cosine_recall': np.float64(0.9653424797788471), 'eval_cosine_ap': np.float64(0.991830463184652), 'eval_runtime': 85.8195, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.701551616191864\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6986618041992188\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601208149892495), 'eval_cosine_accuracy_threshold': np.float32(0.7015516), 'eval_cosine_f1': np.float64(0.9603185710105469), 'eval_cosine_f1_threshold': np.float32(0.6986618), 'eval_cosine_precision': 0.9546707813725243, 'eval_cosine_recall': np.float64(0.966033582471588), 'eval_cosine_ap': np.float64(0.9918970973316491), 'eval_runtime': 85.9679, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.09}\n",
      "{'loss': 0.005, 'grad_norm': 0.09761806577444077, 'learning_rate': 3.8982739625413145e-07, 'epoch': 1.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7197130918502808\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7106675505638123\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9599032456230163), 'eval_cosine_accuracy_threshold': np.float32(0.7197131), 'eval_cosine_f1': np.float64(0.960059998983068), 'eval_cosine_f1_threshold': np.float32(0.71066755), 'eval_cosine_precision': 0.9536111111111111, 'eval_cosine_recall': np.float64(0.9665967031841917), 'eval_cosine_ap': np.float64(0.9917881621576041), 'eval_runtime': 85.9187, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7145152688026428\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145152688026428\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9603127879594553), 'eval_cosine_accuracy_threshold': np.float32(0.71451527), 'eval_cosine_f1': np.float64(0.9604398688558051), 'eval_cosine_f1_threshold': np.float32(0.71451527), 'eval_cosine_precision': 0.9573742974134642, 'eval_cosine_recall': np.float64(0.9635251356608989), 'eval_cosine_ap': np.float64(0.9918810212919096), 'eval_runtime': 86.484, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7176210284233093\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7094452381134033\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.95991604382103), 'eval_cosine_accuracy_threshold': np.float32(0.717621), 'eval_cosine_f1': np.float64(0.960125197211054), 'eval_cosine_f1_threshold': np.float32(0.70944524), 'eval_cosine_precision': 0.954538554948391, 'eval_cosine_recall': np.float64(0.9657776185113136), 'eval_cosine_ap': np.float64(0.9918537800795827), 'eval_runtime': 85.99, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.12}\n",
      "{'loss': 0.0049, 'grad_norm': 0.08935773372650146, 'learning_rate': 3.875321336760926e-07, 'epoch': 1.12}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7149310111999512\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7058407068252563\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601848059793181), 'eval_cosine_accuracy_threshold': np.float32(0.714931), 'eval_cosine_f1': np.float64(0.960469190341991), 'eval_cosine_f1_threshold': np.float32(0.7058407), 'eval_cosine_precision': 0.952752732584496, 'eval_cosine_recall': np.float64(0.9683116617180301), 'eval_cosine_ap': np.float64(0.991894721115846), 'eval_runtime': 85.9833, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.13}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7277920246124268\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7070066928863525\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9595320978806184), 'eval_cosine_accuracy_threshold': np.float32(0.727792), 'eval_cosine_f1': np.float64(0.9597788598092918), 'eval_cosine_f1_threshold': np.float32(0.7070067), 'eval_cosine_precision': 0.9510001005126143, 'eval_cosine_recall': np.float64(0.9687212040544692), 'eval_cosine_ap': np.float64(0.9917196603452627), 'eval_runtime': 85.9396, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.14}\n",
      "{'loss': 0.0049, 'grad_norm': 0.09209485352039337, 'learning_rate': 3.852368710980536e-07, 'epoch': 1.15}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7118713855743408\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7118713855743408\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601976041773318), 'eval_cosine_accuracy_threshold': np.float32(0.7118714), 'eval_cosine_f1': np.float64(0.960323535415391), 'eval_cosine_f1_threshold': np.float32(0.7118714), 'eval_cosine_precision': 0.9572947400549394, 'eval_cosine_recall': np.float64(0.9633715572847343), 'eval_cosine_ap': np.float64(0.9918346565718045), 'eval_runtime': 85.88, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.15}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.717075765132904\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7045053243637085\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9596216852667144), 'eval_cosine_accuracy_threshold': np.float32(0.71707577), 'eval_cosine_f1': np.float64(0.9598591013912471), 'eval_cosine_f1_threshold': np.float32(0.7045053), 'eval_cosine_precision': 0.9503939378732373, 'eval_cosine_recall': np.float64(0.9695146923313197), 'eval_cosine_ap': np.float64(0.991735184598453), 'eval_runtime': 85.8243, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.16}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7131141424179077\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6947383284568787\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9602871915634279), 'eval_cosine_accuracy_threshold': np.float32(0.71311414), 'eval_cosine_f1': np.float64(0.9604353506780154), 'eval_cosine_f1_threshold': np.float32(0.6947383), 'eval_cosine_precision': 0.9520181063749529, 'eval_cosine_recall': np.float64(0.9690027644107709), 'eval_cosine_ap': np.float64(0.9918691779110804), 'eval_runtime': 86.2853, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.17}\n",
      "{'loss': 0.0049, 'grad_norm': 0.16201616823673248, 'learning_rate': 3.8294160852001467e-07, 'epoch': 1.17}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7028986215591431\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7010878920555115\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9604407699395925), 'eval_cosine_accuracy_threshold': np.float32(0.7028986), 'eval_cosine_f1': np.float64(0.9607974938805535), 'eval_cosine_f1_threshold': np.float32(0.7010879), 'eval_cosine_precision': 0.9522109655848563, 'eval_cosine_recall': np.float64(0.9695402887273472), 'eval_cosine_ap': np.float64(0.9918945258019092), 'eval_runtime': 85.894, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7049373388290405\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7049373388290405\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9600184294051397), 'eval_cosine_accuracy_threshold': np.float32(0.70493734), 'eval_cosine_f1': np.float64(0.9603593543802659), 'eval_cosine_f1_threshold': np.float32(0.70493734), 'eval_cosine_precision': 0.9522395571212884, 'eval_cosine_recall': np.float64(0.9686188184703594), 'eval_cosine_ap': np.float64(0.9918267390463403), 'eval_runtime': 85.8653, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.18}\n",
      "{'loss': 0.0049, 'grad_norm': 0.18983109295368195, 'learning_rate': 3.8064634594197575e-07, 'epoch': 1.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7159718871116638\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7129498720169067\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601080167912358), 'eval_cosine_accuracy_threshold': np.float32(0.7159719), 'eval_cosine_f1': np.float64(0.9604053436277745), 'eval_cosine_f1_threshold': np.float32(0.7129499), 'eval_cosine_precision': 0.9529989919354839, 'eval_cosine_recall': np.float64(0.9679277157776185), 'eval_cosine_ap': np.float64(0.9918097894368366), 'eval_runtime': 85.9094, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7118775248527527\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7041043043136597\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9606327429097983), 'eval_cosine_accuracy_threshold': np.float32(0.7118775), 'eval_cosine_f1': np.float64(0.9608860405381348), 'eval_cosine_f1_threshold': np.float32(0.7041043), 'eval_cosine_precision': 0.9547430130894021, 'eval_cosine_recall': np.float64(0.9671086311047404), 'eval_cosine_ap': np.float64(0.991943347090007), 'eval_runtime': 85.8966, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7092480659484863\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7092480659484863\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610038906521962), 'eval_cosine_accuracy_threshold': np.float32(0.70924807), 'eval_cosine_f1': np.float64(0.9611426385257923), 'eval_cosine_f1_threshold': np.float32(0.70924807), 'eval_cosine_precision': 0.9577350242712278, 'eval_cosine_recall': np.float64(0.964574587898024), 'eval_cosine_ap': np.float64(0.9919984420762757), 'eval_runtime': 85.9696, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.21}\n",
      "{'loss': 0.0048, 'grad_norm': 0.07158444076776505, 'learning_rate': 3.783510833639368e-07, 'epoch': 1.22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7267231941223145\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7222162485122681\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9597624654448653), 'eval_cosine_accuracy_threshold': np.float32(0.7267232), 'eval_cosine_f1': np.float64(0.9598355107722566), 'eval_cosine_f1_threshold': np.float32(0.72221625), 'eval_cosine_precision': 0.9577927870523767, 'eval_cosine_recall': np.float64(0.9618869663151428), 'eval_cosine_ap': np.float64(0.9917094892585874), 'eval_runtime': 86.3301, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237499952316284\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7063325643539429\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.95991604382103), 'eval_cosine_accuracy_threshold': np.float32(0.72375), 'eval_cosine_f1': np.float64(0.9601429984026774), 'eval_cosine_f1_threshold': np.float32(0.70633256), 'eval_cosine_precision': 0.9511478374441151, 'eval_cosine_recall': np.float64(0.9693099211631002), 'eval_cosine_ap': np.float64(0.9917510996877446), 'eval_runtime': 85.9449, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.23}\n",
      "{'loss': 0.005, 'grad_norm': 0.17072583734989166, 'learning_rate': 3.760558207858979e-07, 'epoch': 1.24}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7307968139648438\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.717326283454895\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9595320978806184), 'eval_cosine_accuracy_threshold': np.float32(0.7307968), 'eval_cosine_f1': np.float64(0.9596578723620258), 'eval_cosine_f1_threshold': np.float32(0.7173263), 'eval_cosine_precision': 0.951627110965696, 'eval_cosine_recall': np.float64(0.9678253301935088), 'eval_cosine_ap': np.float64(0.9916232928302686), 'eval_runtime': 85.9347, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.24}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7211698293685913\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7129433751106262\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9605175591276748), 'eval_cosine_accuracy_threshold': np.float32(0.7211698), 'eval_cosine_f1': np.float64(0.9607421452476999), 'eval_cosine_f1_threshold': np.float32(0.7129434), 'eval_cosine_precision': 0.9553081108439833, 'eval_cosine_recall': np.float64(0.9662383536398075), 'eval_cosine_ap': np.float64(0.9918729852086803), 'eval_runtime': 85.8155, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7101397514343262\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7101397514343262\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9607223302958944), 'eval_cosine_accuracy_threshold': np.float32(0.71013975), 'eval_cosine_f1': np.float64(0.9608311104871543), 'eval_cosine_f1_threshold': np.float32(0.71013975), 'eval_cosine_precision': 0.9581774214076619, 'eval_cosine_recall': np.float64(0.9634995392648715), 'eval_cosine_ap': np.float64(0.9920077199503043), 'eval_runtime': 85.8114, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.26}\n",
      "{'loss': 0.0049, 'grad_norm': 0.11964136362075806, 'learning_rate': 3.7376055820785897e-07, 'epoch': 1.26}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7242991924285889\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.6989414691925049\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9599416402170574), 'eval_cosine_accuracy_threshold': np.float32(0.7242992), 'eval_cosine_f1': np.float64(0.9600648898014018), 'eval_cosine_f1_threshold': np.float32(0.69894147), 'eval_cosine_precision': 0.9508221413329986, 'eval_cosine_recall': np.float64(0.9694890959352923), 'eval_cosine_ap': np.float64(0.9918841444337043), 'eval_runtime': 85.8829, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.27}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7282203435897827\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7189934253692627\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9597752636428791), 'eval_cosine_accuracy_threshold': np.float32(0.72822034), 'eval_cosine_f1': np.float64(0.9599071345655863), 'eval_cosine_f1_threshold': np.float32(0.7189934), 'eval_cosine_precision': 0.9567705022250477, 'eval_cosine_recall': np.float64(0.963064400532405), 'eval_cosine_ap': np.float64(0.9918476980336723), 'eval_runtime': 86.3352, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.28}\n",
      "{'loss': 0.005, 'grad_norm': 0.1296427696943283, 'learning_rate': 3.7146529562982004e-07, 'epoch': 1.29}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.711915910243988\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.711866557598114\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9603511825534965), 'eval_cosine_accuracy_threshold': np.float32(0.7119159), 'eval_cosine_f1': np.float64(0.9604584673507939), 'eval_cosine_f1_threshold': np.float32(0.71186656), 'eval_cosine_precision': 0.9578665987780041, 'eval_cosine_recall': np.float64(0.963064400532405), 'eval_cosine_ap': np.float64(0.9919778761426989), 'eval_runtime': 85.784, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.29}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7113182544708252\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7113182544708252\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9600440258011672), 'eval_cosine_accuracy_threshold': np.float32(0.71131825), 'eval_cosine_f1': np.float64(0.96019989291451), 'eval_cosine_f1_threshold': np.float32(0.71131825), 'eval_cosine_precision': 0.9564687357139229, 'eval_cosine_recall': np.float64(0.9639602743933654), 'eval_cosine_ap': np.float64(0.9918341044456057), 'eval_runtime': 85.8026, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.29}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7200937867164612\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7170646786689758\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9603767789495239), 'eval_cosine_accuracy_threshold': np.float32(0.7200938), 'eval_cosine_f1': np.float64(0.9604127292230678), 'eval_cosine_f1_threshold': np.float32(0.7170647), 'eval_cosine_precision': 0.9583078491335372, 'eval_cosine_recall': np.float64(0.9625268762158288), 'eval_cosine_ap': np.float64(0.991883112532858), 'eval_runtime': 85.8708, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.3}\n",
      "{'loss': 0.0049, 'grad_norm': 0.23782728612422943, 'learning_rate': 3.691700330517811e-07, 'epoch': 1.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7124497294425964\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7045636177062988\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9608119176819904), 'eval_cosine_accuracy_threshold': np.float32(0.7124497), 'eval_cosine_f1': np.float64(0.9609679143024351), 'eval_cosine_f1_threshold': np.float32(0.7045636), 'eval_cosine_precision': 0.9553042950371832, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9920354674946887), 'eval_runtime': 85.9409, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7293232083320618\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7292583584785461\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9607351284939081), 'eval_cosine_accuracy_threshold': np.float32(0.7293232), 'eval_cosine_f1': np.float64(0.9607542149563794), 'eval_cosine_f1_threshold': np.float32(0.72925836), 'eval_cosine_precision': 0.9602874239247174, 'eval_cosine_recall': np.float64(0.9612214600184295), 'eval_cosine_ap': np.float64(0.9919490288023312), 'eval_runtime': 85.8405, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.32}\n",
      "{'loss': 0.0048, 'grad_norm': 0.07088084518909454, 'learning_rate': 3.6687477047374213e-07, 'epoch': 1.33}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7028675079345703\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7028675079345703\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612598546124705), 'eval_cosine_accuracy_threshold': np.float32(0.7028675), 'eval_cosine_f1': np.float64(0.96138390294309), 'eval_cosine_f1_threshold': np.float32(0.7028675), 'eval_cosine_precision': 0.9583153182939546, 'eval_cosine_recall': np.float64(0.9644722023139142), 'eval_cosine_ap': np.float64(0.992073551796506), 'eval_runtime': 86.3748, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.33}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234097719192505\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.716809868812561\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9598776492269888), 'eval_cosine_accuracy_threshold': np.float32(0.7234098), 'eval_cosine_f1': np.float64(0.960033613017405), 'eval_cosine_f1_threshold': np.float32(0.71680987), 'eval_cosine_precision': 0.9551085552149571, 'eval_cosine_recall': np.float64(0.9650097266304904), 'eval_cosine_ap': np.float64(0.9918004964760437), 'eval_runtime': 85.8987, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7162039279937744\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7104805111885071\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9608375140780178), 'eval_cosine_accuracy_threshold': np.float32(0.7162039), 'eval_cosine_f1': np.float64(0.9609856786096529), 'eval_cosine_f1_threshold': np.float32(0.7104805), 'eval_cosine_precision': 0.9567434544347473, 'eval_cosine_recall': np.float64(0.9652656905907648), 'eval_cosine_ap': np.float64(0.9919979248972757), 'eval_runtime': 85.9987, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.35}\n",
      "{'loss': 0.0048, 'grad_norm': 0.0866554006934166, 'learning_rate': 3.6457950789570326e-07, 'epoch': 1.35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7143718004226685\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7108365893363953\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.960325586157469), 'eval_cosine_accuracy_threshold': np.float32(0.7143718), 'eval_cosine_f1': np.float64(0.9605736589831283), 'eval_cosine_f1_threshold': np.float32(0.7108366), 'eval_cosine_precision': 0.9543008715422509, 'eval_cosine_recall': np.float64(0.9669294563325483), 'eval_cosine_ap': np.float64(0.9918927292134272), 'eval_runtime': 85.925, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.36}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7199636697769165\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7199636697769165\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9607223302958944), 'eval_cosine_accuracy_threshold': np.float32(0.71996367), 'eval_cosine_f1': np.float64(0.960882034287171), 'eval_cosine_f1_threshold': np.float32(0.71996367), 'eval_cosine_precision': 0.9569908853174905, 'eval_cosine_recall': np.float64(0.964804955462271), 'eval_cosine_ap': np.float64(0.9919678938504507), 'eval_runtime': 86.0025, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.37}\n",
      "{'loss': 0.0049, 'grad_norm': 0.1330476552248001, 'learning_rate': 3.6228424531766433e-07, 'epoch': 1.38}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.715072512626648\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7150211930274963\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610550834442511), 'eval_cosine_accuracy_threshold': np.float32(0.7150725), 'eval_cosine_f1': np.float64(0.9611985973860376), 'eval_cosine_f1_threshold': np.float32(0.7150212), 'eval_cosine_precision': 0.9576695378204639, 'eval_cosine_recall': np.float64(0.9647537626702161), 'eval_cosine_ap': np.float64(0.9920821753433713), 'eval_runtime': 85.91, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.38}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7261919975280762\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7116488218307495\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9607479266919218), 'eval_cosine_accuracy_threshold': np.float32(0.726192), 'eval_cosine_f1': np.float64(0.9609342204017249), 'eval_cosine_f1_threshold': np.float32(0.7116488), 'eval_cosine_precision': 0.9551627342386769, 'eval_cosine_recall': np.float64(0.9667758779563838), 'eval_cosine_ap': np.float64(0.99207108272189), 'eval_runtime': 86.2514, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7188502550125122\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7113147974014282\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610038906521962), 'eval_cosine_accuracy_threshold': np.float32(0.71885026), 'eval_cosine_f1': np.float64(0.961129124547015), 'eval_cosine_f1_threshold': np.float32(0.7113148), 'eval_cosine_precision': 0.9549485812466837, 'eval_cosine_recall': np.float64(0.9673901914610423), 'eval_cosine_ap': np.float64(0.9920983217997194), 'eval_runtime': 85.8861, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.4}\n",
      "{'loss': 0.0048, 'grad_norm': 0.10544762015342712, 'learning_rate': 3.599889827396254e-07, 'epoch': 1.4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7198984026908875\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7087790966033936\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9609015050680864), 'eval_cosine_accuracy_threshold': np.float32(0.7198984), 'eval_cosine_f1': np.float64(0.9610736547513039), 'eval_cosine_f1_threshold': np.float32(0.7087791), 'eval_cosine_precision': 0.955313337716858, 'eval_cosine_recall': np.float64(0.966903859936521), 'eval_cosine_ap': np.float64(0.9921109293608812), 'eval_runtime': 85.7934, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7099430561065674\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7051124572753906\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601080167912358), 'eval_cosine_accuracy_threshold': np.float32(0.70994306), 'eval_cosine_f1': np.float64(0.9603827216892544), 'eval_cosine_f1_threshold': np.float32(0.70511246), 'eval_cosine_precision': 0.952310247634387, 'eval_cosine_recall': np.float64(0.968593222074332), 'eval_cosine_ap': np.float64(0.9919207320256743), 'eval_runtime': 85.8602, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.41}\n",
      "{'loss': 0.0048, 'grad_norm': 0.13676758110523224, 'learning_rate': 3.5769372016158643e-07, 'epoch': 1.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7117059826850891\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7116161584854126\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9604279717415788), 'eval_cosine_accuracy_threshold': np.float32(0.711706), 'eval_cosine_f1': np.float64(0.9606875858210853), 'eval_cosine_f1_threshold': np.float32(0.71161616), 'eval_cosine_precision': 0.9544260307194826, 'eval_cosine_recall': np.float64(0.9670318419166581), 'eval_cosine_ap': np.float64(0.9920006216714747), 'eval_runtime': 85.8784, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722710907459259\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.722710907459259\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9601336131872632), 'eval_cosine_accuracy_threshold': np.float32(0.7227109), 'eval_cosine_f1': np.float64(0.960369460948334), 'eval_cosine_f1_threshold': np.float32(0.7227109), 'eval_cosine_precision': 0.9547213720183139, 'eval_cosine_recall': np.float64(0.9660847752636429), 'eval_cosine_ap': np.float64(0.9919081223068585), 'eval_runtime': 85.9572, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.43}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7047432661056519\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7047432661056519\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.960645541107812), 'eval_cosine_accuracy_threshold': np.float32(0.70474327), 'eval_cosine_f1': np.float64(0.9609181377969268), 'eval_cosine_f1_threshold': np.float32(0.70474327), 'eval_cosine_precision': 0.9543079292151566, 'eval_cosine_recall': np.float64(0.9676205590252892), 'eval_cosine_ap': np.float64(0.9921121844859951), 'eval_runtime': 86.3541, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.44}\n",
      "{'loss': 0.0048, 'grad_norm': 0.09311924129724503, 'learning_rate': 3.5539845758354755e-07, 'epoch': 1.45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7108588218688965\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7090376615524292\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9606199447117846), 'eval_cosine_accuracy_threshold': np.float32(0.7108588), 'eval_cosine_f1': np.float64(0.9608887420080586), 'eval_cosine_f1_threshold': np.float32(0.70903766), 'eval_cosine_precision': 0.9543744476707486, 'eval_cosine_recall': np.float64(0.967492577045152), 'eval_cosine_ap': np.float64(0.9920016949207648), 'eval_runtime': 85.9805, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7044427394866943\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7044427394866943\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.960786321285963), 'eval_cosine_accuracy_threshold': np.float32(0.70444274), 'eval_cosine_f1': np.float64(0.9610802022203592), 'eval_cosine_f1_threshold': np.float32(0.70444274), 'eval_cosine_precision': 0.9539311109990418, 'eval_cosine_recall': np.float64(0.9683372581140576), 'eval_cosine_ap': np.float64(0.9920807509561164), 'eval_runtime': 85.8978, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.46}\n",
      "{'loss': 0.0049, 'grad_norm': 0.1047518327832222, 'learning_rate': 3.5310319500550863e-07, 'epoch': 1.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7053155899047852\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7052533626556396\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614518275826763), 'eval_cosine_accuracy_threshold': np.float32(0.7053156), 'eval_cosine_f1': np.float64(0.9616657333401212), 'eval_cosine_f1_threshold': np.float32(0.70525336), 'eval_cosine_precision': 0.9563588497367356, 'eval_cosine_recall': np.float64(0.9670318419166581), 'eval_cosine_ap': np.float64(0.9922337753565398), 'eval_runtime': 85.9781, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7076411247253418\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7076411247253418\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613110474045254), 'eval_cosine_accuracy_threshold': np.float32(0.7076411), 'eval_cosine_f1': np.float64(0.9615056474513248), 'eval_cosine_f1_threshold': np.float32(0.7076411), 'eval_cosine_precision': 0.9566936117375769, 'eval_cosine_recall': np.float64(0.9663663356199447), 'eval_cosine_ap': np.float64(0.9921576979586684), 'eval_runtime': 85.8918, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7224418520927429\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7219969034194946\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9604151735435651), 'eval_cosine_accuracy_threshold': np.float32(0.72244185), 'eval_cosine_f1': np.float64(0.9606323265493146), 'eval_cosine_f1_threshold': np.float32(0.7219969), 'eval_cosine_precision': 0.9553912757284995, 'eval_cosine_recall': np.float64(0.9659311968874782), 'eval_cosine_ap': np.float64(0.991902178758716), 'eval_runtime': 86.3967, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.49}\n",
      "{'loss': 0.0048, 'grad_norm': 0.09921565651893616, 'learning_rate': 3.508079324274697e-07, 'epoch': 1.49}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7207769155502319\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7178329229354858\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610678816422648), 'eval_cosine_accuracy_threshold': np.float32(0.7207769), 'eval_cosine_f1': np.float64(0.9611622004260041), 'eval_cosine_f1_threshold': np.float32(0.7178329), 'eval_cosine_precision': 0.9579000889792806, 'eval_cosine_recall': np.float64(0.9644466059178868), 'eval_cosine_ap': np.float64(0.9921026792452259), 'eval_runtime': 85.882, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222131490707397\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7165330052375793\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9605431555237023), 'eval_cosine_accuracy_threshold': np.float32(0.72221315), 'eval_cosine_f1': np.float64(0.9607621179553056), 'eval_cosine_f1_threshold': np.float32(0.716533), 'eval_cosine_precision': 0.95484768044495, 'eval_cosine_recall': np.float64(0.9667502815603563), 'eval_cosine_ap': np.float64(0.991980295464664), 'eval_runtime': 85.8628, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.51}\n",
      "{'loss': 0.0049, 'grad_norm': 0.1317441165447235, 'learning_rate': 3.485126698494308e-07, 'epoch': 1.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7159185409545898\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7159185409545898\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613494419985666), 'eval_cosine_accuracy_threshold': np.float32(0.71591854), 'eval_cosine_f1': np.float64(0.96153454249032), 'eval_cosine_f1_threshold': np.float32(0.71591854), 'eval_cosine_precision': 0.9569516276239732, 'eval_cosine_recall': np.float64(0.9661615644517252), 'eval_cosine_ap': np.float64(0.9921202665165125), 'eval_runtime': 85.869, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7231857776641846\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7083555459976196\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614774239787038), 'eval_cosine_accuracy_threshold': np.float32(0.7231858), 'eval_cosine_f1': np.float64(0.961602501366346), 'eval_cosine_f1_threshold': np.float32(0.70835555), 'eval_cosine_precision': 0.9550354717362216, 'eval_cosine_recall': np.float64(0.9682604689259752), 'eval_cosine_ap': np.float64(0.9922084917639168), 'eval_runtime': 85.8915, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.52}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7197123765945435\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7197123765945435\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614518275826763), 'eval_cosine_accuracy_threshold': np.float32(0.7197124), 'eval_cosine_f1': np.float64(0.9615943692142911), 'eval_cosine_f1_threshold': np.float32(0.7197124), 'eval_cosine_precision': 0.9580517302708471, 'eval_cosine_recall': np.float64(0.965163305006655), 'eval_cosine_ap': np.float64(0.9921579739968425), 'eval_runtime': 85.8239, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.53}\n",
      "{'loss': 0.0047, 'grad_norm': 0.11852024495601654, 'learning_rate': 3.462174072713918e-07, 'epoch': 1.54}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.708776593208313\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.708776593208313\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.961285451008498), 'eval_cosine_accuracy_threshold': np.float32(0.7087766), 'eval_cosine_f1': np.float64(0.961538950553712), 'eval_cosine_f1_threshold': np.float32(0.7087766), 'eval_cosine_precision': 0.9552838339691282, 'eval_cosine_recall': np.float64(0.9678765229855636), 'eval_cosine_ap': np.float64(0.9920956813192425), 'eval_runtime': 86.3567, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.54}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216286063194275\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7216286063194275\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9615798095628135), 'eval_cosine_accuracy_threshold': np.float32(0.7216286), 'eval_cosine_f1': np.float64(0.9616837698473477), 'eval_cosine_f1_threshold': np.float32(0.7216286), 'eval_cosine_precision': 0.9590885947046843, 'eval_cosine_recall': np.float64(0.9642930275417221), 'eval_cosine_ap': np.float64(0.9921598300086043), 'eval_runtime': 85.873, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.55}\n",
      "{'loss': 0.005, 'grad_norm': 0.1817876696586609, 'learning_rate': 3.439221446933529e-07, 'epoch': 1.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7380090951919556\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144193053245544\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612982492065117), 'eval_cosine_accuracy_threshold': np.float32(0.7380091), 'eval_cosine_f1': np.float64(0.96152238087395), 'eval_cosine_f1_threshold': np.float32(0.7144193), 'eval_cosine_precision': 0.9519580542384787, 'eval_cosine_recall': np.float64(0.971280843657213), 'eval_cosine_ap': np.float64(0.9921315908519075), 'eval_runtime': 85.8698, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7191922664642334\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7053362131118774\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614134329886352), 'eval_cosine_accuracy_threshold': np.float32(0.71919227), 'eval_cosine_f1': np.float64(0.9615130408256648), 'eval_cosine_f1_threshold': np.float32(0.7053362), 'eval_cosine_precision': 0.9549585942233892, 'eval_cosine_recall': np.float64(0.9681580833418655), 'eval_cosine_ap': np.float64(0.9922487021749099), 'eval_runtime': 85.9262, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205836772918701\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7062036395072937\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616310023548684), 'eval_cosine_accuracy_threshold': np.float32(0.7205837), 'eval_cosine_f1': np.float64(0.9617922669073987), 'eval_cosine_f1_threshold': np.float32(0.70620364), 'eval_cosine_precision': 0.9555594856117834, 'eval_cosine_recall': np.float64(0.9681068905498106), 'eval_cosine_ap': np.float64(0.9922386760858113), 'eval_runtime': 85.9039, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.58}\n",
      "{'loss': 0.0048, 'grad_norm': 0.14003407955169678, 'learning_rate': 3.41626882115314e-07, 'epoch': 1.58}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7272910475730896\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7264413833618164\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614774239787038), 'eval_cosine_accuracy_threshold': np.float32(0.72729105), 'eval_cosine_f1': np.float64(0.9614295779158394), 'eval_cosine_f1_threshold': np.float32(0.7264414), 'eval_cosine_precision': 0.9623047926763597, 'eval_cosine_recall': np.float64(0.960555953721716), 'eval_cosine_ap': np.float64(0.992178744002957), 'eval_runtime': 85.9679, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.59}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7321689128875732\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7245780229568481\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612726528104842), 'eval_cosine_accuracy_threshold': np.float32(0.7321689), 'eval_cosine_f1': np.float64(0.9612820840250288), 'eval_cosine_f1_threshold': np.float32(0.724578), 'eval_cosine_precision': 0.9591509097395647, 'eval_cosine_recall': np.float64(0.9634227500767892), 'eval_cosine_ap': np.float64(0.9921404488774023), 'eval_runtime': 86.388, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.6}\n",
      "{'loss': 0.0046, 'grad_norm': 0.15039856731891632, 'learning_rate': 3.3933161953727507e-07, 'epoch': 1.61}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7176185250282288\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7070797681808472\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616182041568547), 'eval_cosine_accuracy_threshold': np.float32(0.7176185), 'eval_cosine_f1': np.float64(0.9617340452580728), 'eval_cosine_f1_threshold': np.float32(0.70707977), 'eval_cosine_precision': 0.9553697716710446, 'eval_cosine_recall': np.float64(0.9681836797378929), 'eval_cosine_ap': np.float64(0.9922075536410107), 'eval_runtime': 85.9021, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.61}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7204755544662476\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7184140682220459\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9611318726323334), 'eval_cosine_accuracy_threshold': np.float32(0.72047555), 'eval_cosine_f1': np.float64(0.961253953678196), 'eval_cosine_f1_threshold': np.float32(0.71841407), 'eval_cosine_precision': 0.9579308591764107, 'eval_cosine_recall': np.float64(0.9646001842940514), 'eval_cosine_ap': np.float64(0.9921007858741593), 'eval_runtime': 85.8192, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7229611873626709\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7228983640670776\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9608375140780178), 'eval_cosine_accuracy_threshold': np.float32(0.7229612), 'eval_cosine_f1': np.float64(0.9609504606824736), 'eval_cosine_f1_threshold': np.float32(0.72289836), 'eval_cosine_precision': 0.9581870005598819, 'eval_cosine_recall': np.float64(0.9637299068291184), 'eval_cosine_ap': np.float64(0.991991248896024), 'eval_runtime': 85.7292, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.62}\n",
      "{'loss': 0.0047, 'grad_norm': 0.09838923811912537, 'learning_rate': 3.370363569592361e-07, 'epoch': 1.63}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7166643142700195\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715664803981781\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9604151735435651), 'eval_cosine_accuracy_threshold': np.float32(0.7166643), 'eval_cosine_f1': np.float64(0.9606180948016904), 'eval_cosine_f1_threshold': np.float32(0.7156648), 'eval_cosine_precision': 0.9554132064006482, 'eval_cosine_recall': np.float64(0.9658800040954234), 'eval_cosine_ap': np.float64(0.9918710501839121), 'eval_runtime': 85.7855, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.63}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.735331654548645\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7239162921905518\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9605047609296611), 'eval_cosine_accuracy_threshold': np.float32(0.73533165), 'eval_cosine_f1': np.float64(0.9605668986260865), 'eval_cosine_f1_threshold': np.float32(0.7239163), 'eval_cosine_precision': 0.9565923744732701, 'eval_cosine_recall': np.float64(0.964574587898024), 'eval_cosine_ap': np.float64(0.9919154421119233), 'eval_runtime': 85.7852, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.64}\n",
      "{'loss': 0.0047, 'grad_norm': 0.13919401168823242, 'learning_rate': 3.3474109438119716e-07, 'epoch': 1.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7266775965690613\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7096885442733765\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9609910924541825), 'eval_cosine_accuracy_threshold': np.float32(0.7266776), 'eval_cosine_f1': np.float64(0.9610683624801272), 'eval_cosine_f1_threshold': np.float32(0.70968854), 'eval_cosine_precision': 0.9551280430770787, 'eval_cosine_recall': np.float64(0.967083034708713), 'eval_cosine_ap': np.float64(0.9920678889058666), 'eval_runtime': 86.3038, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7214914560317993\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7150752544403076\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.960786321285963), 'eval_cosine_accuracy_threshold': np.float32(0.72149146), 'eval_cosine_f1': np.float64(0.9608461018333058), 'eval_cosine_f1_threshold': np.float32(0.71507525), 'eval_cosine_precision': 0.9578278010937301, 'eval_cosine_recall': np.float64(0.9638834852052831), 'eval_cosine_ap': np.float64(0.9920714420790563), 'eval_runtime': 85.8584, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.66}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7254366874694824\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7254366874694824\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612598546124705), 'eval_cosine_accuracy_threshold': np.float32(0.7254367), 'eval_cosine_f1': np.float64(0.9613237079154155), 'eval_cosine_f1_threshold': np.float32(0.7254367), 'eval_cosine_precision': 0.959741816975789, 'eval_cosine_recall': np.float64(0.9629108221562404), 'eval_cosine_ap': np.float64(0.9921574039887731), 'eval_runtime': 85.8454, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.67}\n",
      "{'loss': 0.0048, 'grad_norm': 0.050249066203832626, 'learning_rate': 3.324458318031583e-07, 'epoch': 1.68}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7284123301506042\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7209088206291199\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614390293846626), 'eval_cosine_accuracy_threshold': np.float32(0.72841233), 'eval_cosine_f1': np.float64(0.9614191027163709), 'eval_cosine_f1_threshold': np.float32(0.7209088), 'eval_cosine_precision': 0.9593730087931693, 'eval_cosine_recall': np.float64(0.963473942868844), 'eval_cosine_ap': np.float64(0.9922165603731156), 'eval_runtime': 85.8274, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.68}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.72871994972229\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7238177061080933\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9615670113647998), 'eval_cosine_accuracy_threshold': np.float32(0.72871995), 'eval_cosine_f1': np.float64(0.9615792499840287), 'eval_cosine_f1_threshold': np.float32(0.7238177), 'eval_cosine_precision': 0.9599969385412149, 'eval_cosine_recall': np.float64(0.9631667861165147), 'eval_cosine_ap': np.float64(0.9922209344292962), 'eval_runtime': 86.0064, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.69}\n",
      "{'loss': 0.0048, 'grad_norm': 0.11626428365707397, 'learning_rate': 3.3015056922511936e-07, 'epoch': 1.7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7147799134254456\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.714408814907074\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613878365926077), 'eval_cosine_accuracy_threshold': np.float32(0.7147799), 'eval_cosine_f1': np.float64(0.9615173662929375), 'eval_cosine_f1_threshold': np.float32(0.7144088), 'eval_cosine_precision': 0.9583026111718492, 'eval_cosine_recall': np.float64(0.9647537626702161), 'eval_cosine_ap': np.float64(0.9922049617269494), 'eval_runtime': 85.9099, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7240904569625854\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7119153738021851\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9609782942561688), 'eval_cosine_accuracy_threshold': np.float32(0.72409046), 'eval_cosine_f1': np.float64(0.9610485367776056), 'eval_cosine_f1_threshold': np.float32(0.7119154), 'eval_cosine_precision': 0.9546648482093246, 'eval_cosine_recall': np.float64(0.9675181734411795), 'eval_cosine_ap': np.float64(0.9920565544454965), 'eval_runtime': 86.3781, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7256036996841431\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7256036996841431\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9609526978601413), 'eval_cosine_accuracy_threshold': np.float32(0.7256037), 'eval_cosine_f1': np.float64(0.9610419459873588), 'eval_cosine_f1_threshold': np.float32(0.7256037), 'eval_cosine_precision': 0.9588503579891456, 'eval_cosine_recall': np.float64(0.9632435753045971), 'eval_cosine_ap': np.float64(0.9920564765686625), 'eval_runtime': 85.9116, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.72}\n",
      "{'loss': 0.0047, 'grad_norm': 0.03263077884912491, 'learning_rate': 3.278553066470804e-07, 'epoch': 1.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7272437810897827\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7272437810897827\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.960875908672059), 'eval_cosine_accuracy_threshold': np.float32(0.7272438), 'eval_cosine_f1': np.float64(0.960992229070168), 'eval_cosine_f1_threshold': np.float32(0.7272438), 'eval_cosine_precision': 0.9581435586880741, 'eval_cosine_recall': np.float64(0.9638578888092556), 'eval_cosine_ap': np.float64(0.9919949603935001), 'eval_runtime': 85.9363, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7316588163375854\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7229888439178467\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610422852462374), 'eval_cosine_accuracy_threshold': np.float32(0.7316588), 'eval_cosine_f1': np.float64(0.9611704977421741), 'eval_cosine_f1_threshold': np.float32(0.72298884), 'eval_cosine_precision': 0.9580175965010426, 'eval_cosine_recall': np.float64(0.964344220333777), 'eval_cosine_ap': np.float64(0.9921364685832588), 'eval_runtime': 85.8797, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.74}\n",
      "{'loss': 0.0049, 'grad_norm': 0.039383430033922195, 'learning_rate': 3.2556004406904146e-07, 'epoch': 1.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.727123498916626\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.712470531463623\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612086618204156), 'eval_cosine_accuracy_threshold': np.float32(0.7271235), 'eval_cosine_f1': np.float64(0.961231035536096), 'eval_cosine_f1_threshold': np.float32(0.71247053), 'eval_cosine_precision': 0.9559746835443038, 'eval_cosine_recall': np.float64(0.9665455103921368), 'eval_cosine_ap': np.float64(0.9921767770543815), 'eval_runtime': 85.8742, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7183693051338196\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144564390182495\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621173338793898), 'eval_cosine_accuracy_threshold': np.float32(0.7183693), 'eval_cosine_f1': np.float64(0.9622145681847174), 'eval_cosine_f1_threshold': np.float32(0.71445644), 'eval_cosine_precision': 0.9591068612990183, 'eval_cosine_recall': np.float64(0.9653424797788471), 'eval_cosine_ap': np.float64(0.9923460031000448), 'eval_runtime': 85.8086, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.75}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7307484149932861\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.720253586769104\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612598546124705), 'eval_cosine_accuracy_threshold': np.float32(0.7307484), 'eval_cosine_f1': np.float64(0.961344965930077), 'eval_cosine_f1_threshold': np.float32(0.7202536), 'eval_cosine_precision': 0.956726747281162, 'eval_cosine_recall': np.float64(0.9660079860755606), 'eval_cosine_ap': np.float64(0.9921518472230589), 'eval_runtime': 86.2255, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.76}\n",
      "{'loss': 0.0047, 'grad_norm': 0.02697432041168213, 'learning_rate': 3.232647814910026e-07, 'epoch': 1.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7173573970794678\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173573970794678\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619125627111703), 'eval_cosine_accuracy_threshold': np.float32(0.7173574), 'eval_cosine_f1': np.float64(0.9620117436813889), 'eval_cosine_f1_threshold': np.float32(0.7173574), 'eval_cosine_precision': 0.9595131391322061, 'eval_cosine_recall': np.float64(0.9645233951059691), 'eval_cosine_ap': np.float64(0.9923319668199704), 'eval_runtime': 85.8407, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237815856933594\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7201583981513977\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617973789290468), 'eval_cosine_accuracy_threshold': np.float32(0.7237816), 'eval_cosine_f1': np.float64(0.9618608924219418), 'eval_cosine_f1_threshold': np.float32(0.7201584), 'eval_cosine_precision': 0.9589853191868305, 'eval_cosine_recall': np.float64(0.9647537626702161), 'eval_cosine_ap': np.float64(0.9923153257592938), 'eval_runtime': 85.8647, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.78}\n",
      "{'loss': 0.005, 'grad_norm': 0.16220195591449738, 'learning_rate': 3.2096951891296366e-07, 'epoch': 1.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7326864004135132\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7244085669517517\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9612342582164431), 'eval_cosine_accuracy_threshold': np.float32(0.7326864), 'eval_cosine_f1': np.float64(0.9613554305252549), 'eval_cosine_f1_threshold': np.float32(0.72440857), 'eval_cosine_precision': 0.9583598300816524, 'eval_cosine_recall': np.float64(0.9643698167298045), 'eval_cosine_ap': np.float64(0.992184270688244), 'eval_runtime': 85.8991, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7215640544891357\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7208710312843323\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617973789290468), 'eval_cosine_accuracy_threshold': np.float32(0.72156405), 'eval_cosine_f1': np.float64(0.9618593716059952), 'eval_cosine_f1_threshold': np.float32(0.72087103), 'eval_cosine_precision': 0.9603010588085215, 'eval_cosine_recall': np.float64(0.9634227500767892), 'eval_cosine_ap': np.float64(0.9923135284438613), 'eval_runtime': 85.8203, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7200794816017151\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7200794816017151\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620405446913075), 'eval_cosine_accuracy_threshold': np.float32(0.7200795), 'eval_cosine_f1': np.float64(0.9621190835014942), 'eval_cosine_f1_threshold': np.float32(0.7200795), 'eval_cosine_precision': 0.9601325516186592, 'eval_cosine_recall': np.float64(0.9641138527695301), 'eval_cosine_ap': np.float64(0.992341063281168), 'eval_runtime': 85.9643, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.81}\n",
      "{'loss': 0.0048, 'grad_norm': 0.11906491965055466, 'learning_rate': 3.186742563349247e-07, 'epoch': 1.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7221431732177734\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7195888757705688\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613238456025391), 'eval_cosine_accuracy_threshold': np.float32(0.7221432), 'eval_cosine_f1': np.float64(0.9615296483947349), 'eval_cosine_f1_threshold': np.float32(0.7195889), 'eval_cosine_precision': 0.956440257306387, 'eval_cosine_recall': np.float64(0.966673492372274), 'eval_cosine_ap': np.float64(0.992157054888463), 'eval_runtime': 86.2994, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.82}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7283012866973877\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7170010209083557\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9618869663151428), 'eval_cosine_accuracy_threshold': np.float32(0.7283013), 'eval_cosine_f1': np.float64(0.9620188696061829), 'eval_cosine_f1_threshold': np.float32(0.717001), 'eval_cosine_precision': 0.9571077499936662, 'eval_cosine_recall': np.float64(0.9669806491246032), 'eval_cosine_ap': np.float64(0.9923289797834114), 'eval_runtime': 85.8773, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.83}\n",
      "{'loss': 0.0047, 'grad_norm': 0.09649083018302917, 'learning_rate': 3.1637899375688575e-07, 'epoch': 1.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7260943055152893\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7150421142578125\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620661410873349), 'eval_cosine_accuracy_threshold': np.float32(0.7260943), 'eval_cosine_f1': np.float64(0.9622177794190679), 'eval_cosine_f1_threshold': np.float32(0.7150421), 'eval_cosine_precision': 0.9574263196573832, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.992348171286176), 'eval_runtime': 85.907, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7129446268081665\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7082329988479614\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617205897409645), 'eval_cosine_accuracy_threshold': np.float32(0.7129446), 'eval_cosine_f1': np.float64(0.9619590093068199), 'eval_cosine_f1_threshold': np.float32(0.708233), 'eval_cosine_precision': 0.9556891673403395, 'eval_cosine_recall': np.float64(0.9683116617180301), 'eval_cosine_ap': np.float64(0.9922815029768551), 'eval_runtime': 85.8262, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7257068753242493\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7141618132591248\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616821951469233), 'eval_cosine_accuracy_threshold': np.float32(0.7257069), 'eval_cosine_f1': np.float64(0.9618854910345003), 'eval_cosine_f1_threshold': np.float32(0.7141618), 'eval_cosine_precision': 0.9564931284958619, 'eval_cosine_recall': np.float64(0.9673389986689874), 'eval_cosine_ap': np.float64(0.9922553600819657), 'eval_runtime': 85.8877, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.85}\n",
      "{'loss': 0.0046, 'grad_norm': 0.12230098992586136, 'learning_rate': 3.140837311788468e-07, 'epoch': 1.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7214740514755249\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7214740514755249\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619381591071977), 'eval_cosine_accuracy_threshold': np.float32(0.72147405), 'eval_cosine_f1': np.float64(0.9619449776071656), 'eval_cosine_f1_threshold': np.float32(0.72147405), 'eval_cosine_precision': 0.9617726830766081, 'eval_cosine_recall': np.float64(0.9621173338793898), 'eval_cosine_ap': np.float64(0.9923261860730278), 'eval_runtime': 85.914, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7287047505378723\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7287047505378723\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9614262311866489), 'eval_cosine_accuracy_threshold': np.float32(0.72870475), 'eval_cosine_f1': np.float64(0.9615237317129217), 'eval_cosine_f1_threshold': np.float32(0.72870475), 'eval_cosine_precision': 0.9590994753730964, 'eval_cosine_recall': np.float64(0.9639602743933654), 'eval_cosine_ap': np.float64(0.9921899634010148), 'eval_runtime': 86.3218, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.87}\n",
      "{'loss': 0.0046, 'grad_norm': 0.12637369334697723, 'learning_rate': 3.1178846860080795e-07, 'epoch': 1.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.717214822769165\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7076704502105713\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620789392853486), 'eval_cosine_accuracy_threshold': np.float32(0.7172148), 'eval_cosine_f1': np.float64(0.962158168919005), 'eval_cosine_f1_threshold': np.float32(0.70767045), 'eval_cosine_precision': 0.9575843622442513, 'eval_cosine_recall': np.float64(0.9667758779563838), 'eval_cosine_ap': np.float64(0.9923818850233143), 'eval_runtime': 85.8689, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7203516960144043\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7046651840209961\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617333879389782), 'eval_cosine_accuracy_threshold': np.float32(0.7203517), 'eval_cosine_f1': np.float64(0.9618017559485941), 'eval_cosine_f1_threshold': np.float32(0.7046652), 'eval_cosine_precision': 0.9562775163200243, 'eval_cosine_recall': np.float64(0.9673901914610423), 'eval_cosine_ap': np.float64(0.992324285683671), 'eval_runtime': 85.9929, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.89}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.718864917755127\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7119849920272827\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619381591071977), 'eval_cosine_accuracy_threshold': np.float32(0.7188649), 'eval_cosine_f1': np.float64(0.9620414348108438), 'eval_cosine_f1_threshold': np.float32(0.711985), 'eval_cosine_precision': 0.9572025845686051, 'eval_cosine_recall': np.float64(0.9669294563325483), 'eval_cosine_ap': np.float64(0.9923381314801863), 'eval_runtime': 85.8769, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.9}\n",
      "{'loss': 0.0046, 'grad_norm': 0.14265266060829163, 'learning_rate': 3.09493206022769e-07, 'epoch': 1.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7146514654159546\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7146514654159546\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621813248694584), 'eval_cosine_accuracy_threshold': np.float32(0.71465147), 'eval_cosine_f1': np.float64(0.9621934212714781), 'eval_cosine_f1_threshold': np.float32(0.71465147), 'eval_cosine_precision': 0.9618857595989052, 'eval_cosine_recall': np.float64(0.9625012798198014), 'eval_cosine_ap': np.float64(0.9923817152630136), 'eval_runtime': 85.9185, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7208861112594604\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144862413406372\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613494419985666), 'eval_cosine_accuracy_threshold': np.float32(0.7208861), 'eval_cosine_f1': np.float64(0.9614629794826048), 'eval_cosine_f1_threshold': np.float32(0.71448624), 'eval_cosine_precision': 0.9573879498502614, 'eval_cosine_recall': np.float64(0.9655728473430941), 'eval_cosine_ap': np.float64(0.9922535303119976), 'eval_runtime': 85.8059, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.92}\n",
      "{'loss': 0.0045, 'grad_norm': 0.07092016935348511, 'learning_rate': 3.0719794344473004e-07, 'epoch': 1.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7238245010375977\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7238245010375977\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9618101771270605), 'eval_cosine_accuracy_threshold': np.float32(0.7238245), 'eval_cosine_f1': np.float64(0.9618648399959104), 'eval_cosine_f1_threshold': np.float32(0.7238245), 'eval_cosine_precision': 0.9604900459418071, 'eval_cosine_recall': np.float64(0.9632435753045971), 'eval_cosine_ap': np.float64(0.992311715208062), 'eval_runtime': 86.3444, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7329258322715759\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7209417819976807\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617461861369919), 'eval_cosine_accuracy_threshold': np.float32(0.73292583), 'eval_cosine_f1': np.float64(0.9618848913541297), 'eval_cosine_f1_threshold': np.float32(0.7209418), 'eval_cosine_precision': 0.9561918251719952, 'eval_cosine_recall': np.float64(0.9676461554213167), 'eval_cosine_ap': np.float64(0.9922397924166526), 'eval_runtime': 85.8216, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.94}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7185059785842896\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7184470295906067\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617589843350056), 'eval_cosine_accuracy_threshold': np.float32(0.718506), 'eval_cosine_f1': np.float64(0.9619003901767271), 'eval_cosine_f1_threshold': np.float32(0.718447), 'eval_cosine_precision': 0.9583566238121856, 'eval_cosine_recall': np.float64(0.9654704617589843), 'eval_cosine_ap': np.float64(0.9922501405802808), 'eval_runtime': 85.8221, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.95}\n",
      "{'loss': 0.0046, 'grad_norm': 0.10569847375154495, 'learning_rate': 3.049026808666911e-07, 'epoch': 1.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7130634784698486\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7130634784698486\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9618357735230879), 'eval_cosine_accuracy_threshold': np.float32(0.7130635), 'eval_cosine_f1': np.float64(0.9620204799021855), 'eval_cosine_f1_threshold': np.float32(0.7130635), 'eval_cosine_precision': 0.9573869397688096, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9922796103376924), 'eval_runtime': 85.907, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7169177532196045\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7118595838546753\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617845807310331), 'eval_cosine_accuracy_threshold': np.float32(0.71691775), 'eval_cosine_f1': np.float64(0.9620159865804221), 'eval_cosine_f1_threshold': np.float32(0.7118596), 'eval_cosine_precision': 0.9552784998611917, 'eval_cosine_recall': np.float64(0.9688491860346063), 'eval_cosine_ap': np.float64(0.9922630138912703), 'eval_runtime': 85.7628, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.96}\n",
      "{'loss': 0.0047, 'grad_norm': 0.11957389861345291, 'learning_rate': 3.026074182886522e-07, 'epoch': 1.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7151874303817749\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7151150703430176\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9626292617999386), 'eval_cosine_accuracy_threshold': np.float32(0.71518743), 'eval_cosine_f1': np.float64(0.962753201693964), 'eval_cosine_f1_threshold': np.float32(0.7151151), 'eval_cosine_precision': 0.9595707892595606, 'eval_cosine_recall': np.float64(0.9659567932835057), 'eval_cosine_ap': np.float64(0.9924328403021969), 'eval_runtime': 85.8605, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7098698616027832\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7085342407226562\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620405446913075), 'eval_cosine_accuracy_threshold': np.float32(0.70986986), 'eval_cosine_f1': np.float64(0.9623089724208895), 'eval_cosine_f1_threshold': np.float32(0.70853424), 'eval_cosine_precision': 0.9552344203172681, 'eval_cosine_recall': np.float64(0.9694890959352923), 'eval_cosine_ap': np.float64(0.9923281618703836), 'eval_runtime': 86.2213, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7195541262626648\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7195329666137695\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9615542131667861), 'eval_cosine_accuracy_threshold': np.float32(0.7195541), 'eval_cosine_f1': np.float64(0.9616856282842712), 'eval_cosine_f1_threshold': np.float32(0.71953297), 'eval_cosine_precision': 0.9584095993491967, 'eval_cosine_recall': np.float64(0.964984130234463), 'eval_cosine_ap': np.float64(0.9922430129429196), 'eval_runtime': 85.8165, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 1.99}\n",
      "{'loss': 0.0046, 'grad_norm': 0.15856190025806427, 'learning_rate': 3.003121557106133e-07, 'epoch': 2.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7255765795707703\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7171725034713745\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616182041568547), 'eval_cosine_accuracy_threshold': np.float32(0.7255766), 'eval_cosine_f1': np.float64(0.9617632453493258), 'eval_cosine_f1_threshold': np.float32(0.7171725), 'eval_cosine_precision': 0.9568775494692037, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9922973788853577), 'eval_runtime': 85.7883, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225025296211243\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7223818302154541\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9610294870482237), 'eval_cosine_accuracy_threshold': np.float32(0.7225025), 'eval_cosine_f1': np.float64(0.9611641817694848), 'eval_cosine_f1_threshold': np.float32(0.72238183), 'eval_cosine_precision': 0.9578535295762475, 'eval_cosine_recall': np.float64(0.9644977987099417), 'eval_cosine_ap': np.float64(0.9920868921193874), 'eval_runtime': 85.8354, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.01}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1198677271604538, 'learning_rate': 2.9801689313257434e-07, 'epoch': 2.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.723401665687561\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.723401665687561\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616438005528821), 'eval_cosine_accuracy_threshold': np.float32(0.72340167), 'eval_cosine_f1': np.float64(0.9617763720076014), 'eval_cosine_f1_threshold': np.float32(0.72340167), 'eval_cosine_precision': 0.9584636111746613, 'eval_cosine_recall': np.float64(0.9651121122146001), 'eval_cosine_ap': np.float64(0.9922411204569827), 'eval_runtime': 86.2242, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7245899438858032\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7162172198295593\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9616182041568547), 'eval_cosine_accuracy_threshold': np.float32(0.72458994), 'eval_cosine_f1': np.float64(0.9617283322306479), 'eval_cosine_f1_threshold': np.float32(0.7162172), 'eval_cosine_precision': 0.9561323618700668, 'eval_cosine_recall': np.float64(0.9673901914610423), 'eval_cosine_ap': np.float64(0.992229607991528), 'eval_runtime': 85.8376, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.03}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.720961332321167\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7208284139633179\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.961925360909184), 'eval_cosine_accuracy_threshold': np.float32(0.72096133), 'eval_cosine_f1': np.float64(0.9620772731328634), 'eval_cosine_f1_threshold': np.float32(0.7208284), 'eval_cosine_precision': 0.9582539803458521, 'eval_cosine_recall': np.float64(0.9659311968874782), 'eval_cosine_ap': np.float64(0.9922796316552455), 'eval_runtime': 85.7763, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.04}\n",
      "{'loss': 0.0046, 'grad_norm': 0.08398430049419403, 'learning_rate': 2.957216305545354e-07, 'epoch': 2.04}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7288987040519714\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7140755653381348\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9613366438005528), 'eval_cosine_accuracy_threshold': np.float32(0.7288987), 'eval_cosine_f1': np.float64(0.9615365072912962), 'eval_cosine_f1_threshold': np.float32(0.71407557), 'eval_cosine_precision': 0.9544079080088763, 'eval_cosine_recall': np.float64(0.968772396846524), 'eval_cosine_ap': np.float64(0.9922066388099114), 'eval_runtime': 85.8341, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7201375961303711\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7201375961303711\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9622965086515819), 'eval_cosine_accuracy_threshold': np.float32(0.7201376), 'eval_cosine_f1': np.float64(0.9623591342345335), 'eval_cosine_f1_threshold': np.float32(0.7201376), 'eval_cosine_precision': 0.9607633042502168, 'eval_cosine_recall': np.float64(0.9639602743933654), 'eval_cosine_ap': np.float64(0.9923970635740325), 'eval_runtime': 85.7748, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.06}\n",
      "{'loss': 0.0048, 'grad_norm': 0.10773536562919617, 'learning_rate': 2.934263679764965e-07, 'epoch': 2.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7060079574584961\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7059856653213501\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624116924337053), 'eval_cosine_accuracy_threshold': np.float32(0.70600796), 'eval_cosine_f1': np.float64(0.9625712064636991), 'eval_cosine_f1_threshold': np.float32(0.70598567), 'eval_cosine_precision': 0.9585035912794092, 'eval_cosine_recall': np.float64(0.966673492372274), 'eval_cosine_ap': np.float64(0.9924578229483254), 'eval_runtime': 85.8556, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222907543182373\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7104234099388123\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621173338793898), 'eval_cosine_accuracy_threshold': np.float32(0.72229075), 'eval_cosine_f1': np.float64(0.9621712409817919), 'eval_cosine_f1_threshold': np.float32(0.7104234), 'eval_cosine_precision': 0.9566328466968601, 'eval_cosine_recall': np.float64(0.9677741374014539), 'eval_cosine_ap': np.float64(0.9924216694267188), 'eval_runtime': 86.2746, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7100666761398315\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7100666761398315\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9622709122555544), 'eval_cosine_accuracy_threshold': np.float32(0.7100667), 'eval_cosine_f1': np.float64(0.9623950812562185), 'eval_cosine_f1_threshold': np.float32(0.7100667), 'eval_cosine_precision': 0.9592381630473478, 'eval_cosine_recall': np.float64(0.9655728473430941), 'eval_cosine_ap': np.float64(0.992449694114982), 'eval_runtime': 85.9601, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.08}\n",
      "{'loss': 0.0046, 'grad_norm': 0.07731888443231583, 'learning_rate': 2.911311053984576e-07, 'epoch': 2.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.709091067314148\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7025793790817261\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623477014436367), 'eval_cosine_accuracy_threshold': np.float32(0.70909107), 'eval_cosine_f1': np.float64(0.9625577282731772), 'eval_cosine_f1_threshold': np.float32(0.7025794), 'eval_cosine_precision': 0.9568967697872663, 'eval_cosine_recall': np.float64(0.9682860653220027), 'eval_cosine_ap': np.float64(0.9925010849604745), 'eval_runtime': 85.8191, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7170929908752441\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7090456485748291\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621045356813761), 'eval_cosine_accuracy_threshold': np.float32(0.717093), 'eval_cosine_f1': np.float64(0.9623278971701468), 'eval_cosine_f1_threshold': np.float32(0.70904565), 'eval_cosine_precision': 0.9550978217022993, 'eval_cosine_recall': np.float64(0.9696682707074844), 'eval_cosine_ap': np.float64(0.992445567772174), 'eval_runtime': 85.9623, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.1}\n",
      "{'loss': 0.0047, 'grad_norm': 0.14379072189331055, 'learning_rate': 2.8883584282041863e-07, 'epoch': 2.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7174363136291504\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.709109902381897\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621045356813761), 'eval_cosine_accuracy_threshold': np.float32(0.7174363), 'eval_cosine_f1': np.float64(0.9624071605408493), 'eval_cosine_f1_threshold': np.float32(0.7091099), 'eval_cosine_precision': 0.9547824772652845, 'eval_cosine_recall': np.float64(0.9701546022320058), 'eval_cosine_ap': np.float64(0.9924383862319313), 'eval_runtime': 85.9535, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7272500991821289\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7107359766960144\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9622325176615133), 'eval_cosine_accuracy_threshold': np.float32(0.7272501), 'eval_cosine_f1': np.float64(0.9624190064794816), 'eval_cosine_f1_threshold': np.float32(0.710736), 'eval_cosine_precision': 0.95545128903688, 'eval_cosine_recall': np.float64(0.9694890959352923), 'eval_cosine_ap': np.float64(0.9924268431701864), 'eval_runtime': 85.8855, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.12}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7267058491706848\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7100406885147095\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624500870277465), 'eval_cosine_accuracy_threshold': np.float32(0.72670585), 'eval_cosine_f1': np.float64(0.9626619253238508), 'eval_cosine_f1_threshold': np.float32(0.7100407), 'eval_cosine_precision': 0.9553337366404517, 'eval_cosine_recall': np.float64(0.9701034094399509), 'eval_cosine_ap': np.float64(0.9924771584407768), 'eval_runtime': 86.3011, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.13}\n",
      "{'loss': 0.0046, 'grad_norm': 0.19384390115737915, 'learning_rate': 2.865405802423797e-07, 'epoch': 2.13}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7217493653297424\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7135698199272156\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621813248694584), 'eval_cosine_accuracy_threshold': np.float32(0.72174937), 'eval_cosine_f1': np.float64(0.9622519001362241), 'eval_cosine_f1_threshold': np.float32(0.7135698), 'eval_cosine_precision': 0.9572430912637098, 'eval_cosine_recall': np.float64(0.9673134022729599), 'eval_cosine_ap': np.float64(0.9924309011674919), 'eval_runtime': 85.7792, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.14}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7180315256118774\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7058913707733154\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962475683423774), 'eval_cosine_accuracy_threshold': np.float32(0.7180315), 'eval_cosine_f1': np.float64(0.9625306734815832), 'eval_cosine_f1_threshold': np.float32(0.7058914), 'eval_cosine_precision': 0.9562691054240456, 'eval_cosine_recall': np.float64(0.9688747824306337), 'eval_cosine_ap': np.float64(0.992520599870216), 'eval_runtime': 85.7609, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.15}\n",
      "{'loss': 0.0046, 'grad_norm': 0.056914716958999634, 'learning_rate': 2.842453176643408e-07, 'epoch': 2.16}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7179392576217651\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7179392576217651\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619381591071977), 'eval_cosine_accuracy_threshold': np.float32(0.71793926), 'eval_cosine_f1': np.float64(0.9621387651177594), 'eval_cosine_f1_threshold': np.float32(0.71793926), 'eval_cosine_precision': 0.9570943721189403, 'eval_cosine_recall': np.float64(0.9672366130848776), 'eval_cosine_ap': np.float64(0.992383954741801), 'eval_runtime': 85.777, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.16}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7146270275115967\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7146270275115967\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627188491860346), 'eval_cosine_accuracy_threshold': np.float32(0.714627), 'eval_cosine_f1': np.float64(0.9627584090821923), 'eval_cosine_f1_threshold': np.float32(0.714627), 'eval_cosine_precision': 0.9617378866440193, 'eval_cosine_recall': np.float64(0.9637810996211733), 'eval_cosine_ap': np.float64(0.9925606168706353), 'eval_runtime': 85.8616, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.17}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7189924716949463\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7189924716949463\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620405446913075), 'eval_cosine_accuracy_threshold': np.float32(0.7189925), 'eval_cosine_f1': np.float64(0.962179944914822), 'eval_cosine_f1_threshold': np.float32(0.7189925), 'eval_cosine_precision': 0.9586594166073787, 'eval_cosine_recall': np.float64(0.9657264257192587), 'eval_cosine_ap': np.float64(0.9924225469076063), 'eval_runtime': 85.7785, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.18}\n",
      "{'loss': 0.0045, 'grad_norm': 0.04725750535726547, 'learning_rate': 2.8195005508630185e-07, 'epoch': 2.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7314155101776123\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7131510972976685\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9618997645131565), 'eval_cosine_accuracy_threshold': np.float32(0.7314155), 'eval_cosine_f1': np.float64(0.9620063301597794), 'eval_cosine_f1_threshold': np.float32(0.7131511), 'eval_cosine_precision': 0.9555084210792112, 'eval_cosine_recall': np.float64(0.968593222074332), 'eval_cosine_ap': np.float64(0.9924103690942846), 'eval_runtime': 86.2944, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7256699800491333\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7245694398880005\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623093068495956), 'eval_cosine_accuracy_threshold': np.float32(0.72567), 'eval_cosine_f1': np.float64(0.9624021754394924), 'eval_cosine_f1_threshold': np.float32(0.72456944), 'eval_cosine_precision': 0.9600366776190112, 'eval_cosine_recall': np.float64(0.9647793590662435), 'eval_cosine_ap': np.float64(0.992464775021116), 'eval_runtime': 85.8079, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.19}\n",
      "{'loss': 0.0045, 'grad_norm': 0.07989192754030228, 'learning_rate': 2.79654792508263e-07, 'epoch': 2.2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7173370718955994\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7168371677398682\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.7173371), 'eval_cosine_f1': np.float64(0.9629582322135649), 'eval_cosine_f1_threshold': np.float32(0.71683717), 'eval_cosine_precision': 0.9610677680893376, 'eval_cosine_recall': np.float64(0.9648561482543258), 'eval_cosine_ap': np.float64(0.9925671096569072), 'eval_runtime': 85.8673, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7173794507980347\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173794507980347\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627572437800758), 'eval_cosine_accuracy_threshold': np.float32(0.71737945), 'eval_cosine_f1': np.float64(0.9628485343682974), 'eval_cosine_f1_threshold': np.float32(0.71737945), 'eval_cosine_precision': 0.9604941416199695, 'eval_cosine_recall': np.float64(0.9652144977987099), 'eval_cosine_ap': np.float64(0.9925811879293871), 'eval_runtime': 85.8044, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.21}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7277695536613464\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7275838851928711\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623221050476093), 'eval_cosine_accuracy_threshold': np.float32(0.72776955), 'eval_cosine_f1': np.float64(0.9623211406046024), 'eval_cosine_f1_threshold': np.float32(0.7275839), 'eval_cosine_precision': 0.9623457738186658, 'eval_cosine_recall': np.float64(0.9622965086515819), 'eval_cosine_ap': np.float64(0.9924186139929309), 'eval_runtime': 85.8874, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.22}\n",
      "{'loss': 0.0046, 'grad_norm': 0.06957326829433441, 'learning_rate': 2.77359529930224e-07, 'epoch': 2.23}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.725935697555542\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237162590026855\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621813248694584), 'eval_cosine_accuracy_threshold': np.float32(0.7259357), 'eval_cosine_f1': np.float64(0.9622120522588399), 'eval_cosine_f1_threshold': np.float32(0.72371626), 'eval_cosine_precision': 0.96110628734869, 'eval_cosine_recall': np.float64(0.9633203644926794), 'eval_cosine_ap': np.float64(0.992410137576555), 'eval_runtime': 85.8473, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.23}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7313551902770996\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7313551902770996\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9617973789290468), 'eval_cosine_accuracy_threshold': np.float32(0.7313552), 'eval_cosine_f1': np.float64(0.9618759339438293), 'eval_cosine_f1_threshold': np.float32(0.7313552), 'eval_cosine_precision': 0.9599021132325576, 'eval_cosine_recall': np.float64(0.9638578888092556), 'eval_cosine_ap': np.float64(0.9922619472049364), 'eval_runtime': 86.2857, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.24}\n",
      "{'loss': 0.0046, 'grad_norm': 0.14424921572208405, 'learning_rate': 2.7506426735218507e-07, 'epoch': 2.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246774435043335\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7171194553375244\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624244906317191), 'eval_cosine_accuracy_threshold': np.float32(0.72467744), 'eval_cosine_f1': np.float64(0.9624556839339914), 'eval_cosine_f1_threshold': np.float32(0.71711946), 'eval_cosine_precision': 0.959055558379505, 'eval_cosine_recall': np.float64(0.9658800040954234), 'eval_cosine_ap': np.float64(0.9924716203813185), 'eval_runtime': 85.775, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7130982875823975\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7104431390762329\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623604996416505), 'eval_cosine_accuracy_threshold': np.float32(0.7130983), 'eval_cosine_f1': np.float64(0.9625119419145277), 'eval_cosine_f1_threshold': np.float32(0.71044314), 'eval_cosine_precision': 0.9580089763420139, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9925054160530292), 'eval_runtime': 85.8451, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.26}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7168827056884766\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7159614562988281\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619893518992526), 'eval_cosine_accuracy_threshold': np.float32(0.7168827), 'eval_cosine_f1': np.float64(0.96213087162748), 'eval_cosine_f1_threshold': np.float32(0.71596146), 'eval_cosine_precision': 0.9585619918699188, 'eval_cosine_recall': np.float64(0.9657264257192587), 'eval_cosine_ap': np.float64(0.9924291964040131), 'eval_runtime': 85.8699, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.27}\n",
      "{'loss': 0.0046, 'grad_norm': 0.0984821617603302, 'learning_rate': 2.7276900477414615e-07, 'epoch': 2.27}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7240210175514221\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7156798839569092\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9618229753250742), 'eval_cosine_accuracy_threshold': np.float32(0.724021), 'eval_cosine_f1': np.float64(0.9619529304122401), 'eval_cosine_f1_threshold': np.float32(0.7156799), 'eval_cosine_precision': 0.95520391681809, 'eval_cosine_recall': np.float64(0.9687979932425514), 'eval_cosine_ap': np.float64(0.9923825047886685), 'eval_runtime': 85.8369, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7094092965126038\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.709230899810791\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624500870277465), 'eval_cosine_accuracy_threshold': np.float32(0.7094093), 'eval_cosine_f1': np.float64(0.962633723892002), 'eval_cosine_f1_threshold': np.float32(0.7092309), 'eval_cosine_precision': 0.9579488999290277, 'eval_cosine_recall': np.float64(0.9673645950650148), 'eval_cosine_ap': np.float64(0.9925622167194149), 'eval_runtime': 85.7943, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.29}\n",
      "{'loss': 0.0046, 'grad_norm': 0.06813400983810425, 'learning_rate': 2.7047374219610727e-07, 'epoch': 2.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7282461524009705\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7241898775100708\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9619765537012389), 'eval_cosine_accuracy_threshold': np.float32(0.72824615), 'eval_cosine_f1': np.float64(0.9620259634059083), 'eval_cosine_f1_threshold': np.float32(0.7241899), 'eval_cosine_precision': 0.9604551484845393, 'eval_cosine_recall': np.float64(0.9636019248489812), 'eval_cosine_ap': np.float64(0.9923995933239378), 'eval_runtime': 86.2553, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7187816500663757\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7186331748962402\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623604996416505), 'eval_cosine_accuracy_threshold': np.float32(0.71878165), 'eval_cosine_f1': np.float64(0.9624426935012196), 'eval_cosine_f1_threshold': np.float32(0.7186332), 'eval_cosine_precision': 0.9603455745559265, 'eval_cosine_recall': np.float64(0.9645489915019965), 'eval_cosine_ap': np.float64(0.9924761751438382), 'eval_runtime': 85.9374, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7255743741989136\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7197954058647156\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624244906317191), 'eval_cosine_accuracy_threshold': np.float32(0.7255744), 'eval_cosine_f1': np.float64(0.962500797448166), 'eval_cosine_f1_threshold': np.float32(0.7197954), 'eval_cosine_precision': 0.9595746304729438, 'eval_cosine_recall': np.float64(0.9654448653629569), 'eval_cosine_ap': np.float64(0.992505468165489), 'eval_runtime': 85.8612, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.31}\n",
      "{'loss': 0.0046, 'grad_norm': 0.09146023541688919, 'learning_rate': 2.681784796180683e-07, 'epoch': 2.32}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7329877614974976\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7258144617080688\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962334903245623), 'eval_cosine_accuracy_threshold': np.float32(0.73298776), 'eval_cosine_f1': np.float64(0.9623478005340557), 'eval_cosine_f1_threshold': np.float32(0.72581446), 'eval_cosine_precision': 0.9607152878753094, 'eval_cosine_recall': np.float64(0.9639858707893929), 'eval_cosine_ap': np.float64(0.9924663065607425), 'eval_runtime': 85.95, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.32}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.712770402431488\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.712770402431488\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628468311661718), 'eval_cosine_accuracy_threshold': np.float32(0.7127704), 'eval_cosine_f1': np.float64(0.9628805605636325), 'eval_cosine_f1_threshold': np.float32(0.7127704), 'eval_cosine_precision': 0.9620072050895526, 'eval_cosine_recall': np.float64(0.9637555032251459), 'eval_cosine_ap': np.float64(0.992575142098323), 'eval_runtime': 85.7568, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.33}\n",
      "{'loss': 0.0046, 'grad_norm': 0.1833319365978241, 'learning_rate': 2.6588321704002937e-07, 'epoch': 2.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237013578414917\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237013578414917\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620405446913075), 'eval_cosine_accuracy_threshold': np.float32(0.72370136), 'eval_cosine_f1': np.float64(0.9621480895377627), 'eval_cosine_f1_threshold': np.float32(0.72370136), 'eval_cosine_precision': 0.9594298803766862, 'eval_cosine_recall': np.float64(0.9648817446503533), 'eval_cosine_ap': np.float64(0.9924079678079001), 'eval_runtime': 85.8155, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7255450487136841\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.713754415512085\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9625524726118563), 'eval_cosine_accuracy_threshold': np.float32(0.72554505), 'eval_cosine_f1': np.float64(0.962653593939008), 'eval_cosine_f1_threshold': np.float32(0.7137544), 'eval_cosine_precision': 0.9577875186864976, 'eval_cosine_recall': np.float64(0.9675693662332343), 'eval_cosine_ap': np.float64(0.9925457625722408), 'eval_runtime': 86.252, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7217047214508057\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7217047214508057\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9621045356813761), 'eval_cosine_accuracy_threshold': np.float32(0.7217047), 'eval_cosine_f1': np.float64(0.9622297340391606), 'eval_cosine_f1_threshold': np.float32(0.7217047), 'eval_cosine_precision': 0.9590612047702596, 'eval_cosine_recall': np.float64(0.9654192689669294), 'eval_cosine_ap': np.float64(0.9924406611350405), 'eval_runtime': 85.7741, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.36}\n",
      "{'loss': 0.0047, 'grad_norm': 0.10228356719017029, 'learning_rate': 2.6358795446199044e-07, 'epoch': 2.36}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7355505228042603\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7150661945343018\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9620405446913075), 'eval_cosine_accuracy_threshold': np.float32(0.7355505), 'eval_cosine_f1': np.float64(0.9620895787589954), 'eval_cosine_f1_threshold': np.float32(0.7150662), 'eval_cosine_precision': 0.9541575409712257, 'eval_cosine_recall': np.float64(0.9701546022320058), 'eval_cosine_ap': np.float64(0.9924247084423004), 'eval_runtime': 85.7788, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.37}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220112681388855\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7220112681388855\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9626676563939798), 'eval_cosine_accuracy_threshold': np.float32(0.72201127), 'eval_cosine_f1': np.float64(0.962738711119627), 'eval_cosine_f1_threshold': np.float32(0.72201127), 'eval_cosine_precision': 0.960909809521381, 'eval_cosine_recall': np.float64(0.964574587898024), 'eval_cosine_ap': np.float64(0.992571855690738), 'eval_runtime': 85.7273, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.38}\n",
      "{'loss': 0.0046, 'grad_norm': 0.09397363662719727, 'learning_rate': 2.612926918839515e-07, 'epoch': 2.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7253755331039429\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7177891731262207\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627444455820621), 'eval_cosine_accuracy_threshold': np.float32(0.72537553), 'eval_cosine_f1': np.float64(0.9628203493561138), 'eval_cosine_f1_threshold': np.float32(0.7177892), 'eval_cosine_precision': 0.95922463289467, 'eval_cosine_recall': np.float64(0.9664431248080271), 'eval_cosine_ap': np.float64(0.9926129099944951), 'eval_runtime': 85.7505, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7244491577148438\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7239423990249634\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.72444916), 'eval_cosine_f1': np.float64(0.9629620169097551), 'eval_cosine_f1_threshold': np.float32(0.7239424), 'eval_cosine_precision': 0.9609737445832272, 'eval_cosine_recall': np.float64(0.9649585338384356), 'eval_cosine_ap': np.float64(0.9926255745911099), 'eval_runtime': 85.8505, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7090471982955933\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7090471982955933\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630004095423365), 'eval_cosine_accuracy_threshold': np.float32(0.7090472), 'eval_cosine_f1': np.float64(0.9631508508061947), 'eval_cosine_f1_threshold': np.float32(0.7090472), 'eval_cosine_precision': 0.9592505141290274, 'eval_cosine_recall': np.float64(0.967083034708713), 'eval_cosine_ap': np.float64(0.9927058276649426), 'eval_runtime': 86.3089, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.41}\n",
      "{'loss': 0.0044, 'grad_norm': 0.06045418605208397, 'learning_rate': 2.589974293059126e-07, 'epoch': 2.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7213353514671326\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213353514671326\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623477014436367), 'eval_cosine_accuracy_threshold': np.float32(0.72133535), 'eval_cosine_f1': np.float64(0.9624169647419518), 'eval_cosine_f1_threshold': np.float32(0.72133535), 'eval_cosine_precision': 0.9606498010813016, 'eval_cosine_recall': np.float64(0.9641906419576124), 'eval_cosine_ap': np.float64(0.9924870446310918), 'eval_runtime': 85.8045, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.726872980594635\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7234264016151428\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624244906317191), 'eval_cosine_accuracy_threshold': np.float32(0.726873), 'eval_cosine_f1': np.float64(0.9625169043913143), 'eval_cosine_f1_threshold': np.float32(0.7234264), 'eval_cosine_precision': 0.9595055196622069, 'eval_cosine_recall': np.float64(0.9655472509470666), 'eval_cosine_ap': np.float64(0.9924920325597631), 'eval_runtime': 85.8693, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.42}\n",
      "{'loss': 0.0046, 'grad_norm': 0.08403370529413223, 'learning_rate': 2.5670216672787366e-07, 'epoch': 2.43}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7299271821975708\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205451726913452\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624628852257602), 'eval_cosine_accuracy_threshold': np.float32(0.7299272), 'eval_cosine_f1': np.float64(0.9625347018821792), 'eval_cosine_f1_threshold': np.float32(0.7205452), 'eval_cosine_precision': 0.957777890415125, 'eval_cosine_recall': np.float64(0.9673389986689874), 'eval_cosine_ap': np.float64(0.9925444500026103), 'eval_runtime': 85.7863, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.43}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7238029837608337\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7238029837608337\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627188491860346), 'eval_cosine_accuracy_threshold': np.float32(0.723803), 'eval_cosine_f1': np.float64(0.9627164633756127), 'eval_cosine_f1_threshold': np.float32(0.723803), 'eval_cosine_precision': 0.9627780764406215, 'eval_cosine_recall': np.float64(0.962654858195966), 'eval_cosine_ap': np.float64(0.9925921093507711), 'eval_runtime': 85.9294, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.44}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.725023627281189\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7104306221008301\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9625012798198014), 'eval_cosine_accuracy_threshold': np.float32(0.7250236), 'eval_cosine_f1': np.float64(0.9626315521887233), 'eval_cosine_f1_threshold': np.float32(0.7104306), 'eval_cosine_precision': 0.9560694809129469, 'eval_cosine_recall': np.float64(0.9692843247670728), 'eval_cosine_ap': np.float64(0.9925373844292199), 'eval_runtime': 85.847, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.45}\n",
      "{'loss': 0.0046, 'grad_norm': 0.1410447210073471, 'learning_rate': 2.5440690414983473e-07, 'epoch': 2.46}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7266442775726318\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7160857915878296\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624116924337053), 'eval_cosine_accuracy_threshold': np.float32(0.7266443), 'eval_cosine_f1': np.float64(0.9625826506822264), 'eval_cosine_f1_threshold': np.float32(0.7160858), 'eval_cosine_precision': 0.9582244768547876, 'eval_cosine_recall': np.float64(0.9669806491246032), 'eval_cosine_ap': np.float64(0.9925372451220597), 'eval_runtime': 86.3455, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.46}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7096511125564575\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7067370414733887\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631411897204873), 'eval_cosine_accuracy_threshold': np.float32(0.7096511), 'eval_cosine_f1': np.float64(0.9632678023112107), 'eval_cosine_f1_threshold': np.float32(0.70673704), 'eval_cosine_precision': 0.958979172480276, 'eval_cosine_recall': np.float64(0.9675949626292618), 'eval_cosine_ap': np.float64(0.9927280317866629), 'eval_runtime': 85.8585, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.47}\n",
      "{'loss': 0.0046, 'grad_norm': 0.1512397676706314, 'learning_rate': 2.521116415717958e-07, 'epoch': 2.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7113274335861206\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7099016904830933\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631027951264461), 'eval_cosine_accuracy_threshold': np.float32(0.71132743), 'eval_cosine_f1': np.float64(0.9632350128160986), 'eval_cosine_f1_threshold': np.float32(0.7099017), 'eval_cosine_precision': 0.959795674604183, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9926747250852397), 'eval_runtime': 85.8372, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7230430841445923\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7230430841445923\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9622965086515819), 'eval_cosine_accuracy_threshold': np.float32(0.7230431), 'eval_cosine_f1': np.float64(0.9624081257656186), 'eval_cosine_f1_threshold': np.float32(0.7230431), 'eval_cosine_precision': 0.9595674300254453, 'eval_cosine_recall': np.float64(0.9652656905907648), 'eval_cosine_ap': np.float64(0.992500634755312), 'eval_runtime': 85.9048, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.49}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7210935354232788\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7125327587127686\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962795638374117), 'eval_cosine_accuracy_threshold': np.float32(0.72109354), 'eval_cosine_f1': np.float64(0.9629648490712568), 'eval_cosine_f1_threshold': np.float32(0.71253276), 'eval_cosine_precision': 0.9579523290863498, 'eval_cosine_recall': np.float64(0.9680301013617283), 'eval_cosine_ap': np.float64(0.9926175038612759), 'eval_runtime': 85.7773, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.5}\n",
      "{'loss': 0.0046, 'grad_norm': 0.13282838463783264, 'learning_rate': 2.498163789937569e-07, 'epoch': 2.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246671915054321\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7246671915054321\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627572437800758), 'eval_cosine_accuracy_threshold': np.float32(0.7246672), 'eval_cosine_f1': np.float64(0.9628361983091108), 'eval_cosine_f1_threshold': np.float32(0.7246672), 'eval_cosine_precision': 0.96079930672376, 'eval_cosine_recall': np.float64(0.9648817446503533), 'eval_cosine_ap': np.float64(0.992549782593657), 'eval_runtime': 85.7743, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7254500389099121\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7228575944900513\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629108221562404), 'eval_cosine_accuracy_threshold': np.float32(0.72545004), 'eval_cosine_f1': np.float64(0.9629866067438683), 'eval_cosine_f1_threshold': np.float32(0.7228576), 'eval_cosine_precision': 0.9606929053623742, 'eval_cosine_recall': np.float64(0.9652912869867922), 'eval_cosine_ap': np.float64(0.9926011707189982), 'eval_runtime': 86.3112, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.52}\n",
      "{'loss': 0.0046, 'grad_norm': 0.14715130627155304, 'learning_rate': 2.4752111641571795e-07, 'epoch': 2.52}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7229857444763184\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7223778963088989\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963064400532405), 'eval_cosine_accuracy_threshold': np.float32(0.72298574), 'eval_cosine_f1': np.float64(0.9631521156252394), 'eval_cosine_f1_threshold': np.float32(0.7223779), 'eval_cosine_precision': 0.9608702297854995, 'eval_cosine_recall': np.float64(0.9654448653629569), 'eval_cosine_ap': np.float64(0.9926411227975334), 'eval_runtime': 85.8334, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.52}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7176699638366699\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7175539135932922\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.96311559332446), 'eval_cosine_accuracy_threshold': np.float32(0.71766996), 'eval_cosine_f1': np.float64(0.9632416713432989), 'eval_cosine_f1_threshold': np.float32(0.7175539), 'eval_cosine_precision': 0.959960341671751, 'eval_cosine_recall': np.float64(0.9665455103921368), 'eval_cosine_ap': np.float64(0.9926614096631181), 'eval_runtime': 85.7904, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7156485319137573\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7146533131599426\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631795843145285), 'eval_cosine_accuracy_threshold': np.float32(0.71564853), 'eval_cosine_f1': np.float64(0.9632871817775792), 'eval_cosine_f1_threshold': np.float32(0.7146533), 'eval_cosine_precision': 0.9604804437997811, 'eval_cosine_recall': np.float64(0.9661103716596703), 'eval_cosine_ap': np.float64(0.9926661226850573), 'eval_runtime': 85.933, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.54}\n",
      "{'loss': 0.0046, 'grad_norm': 0.14542581140995026, 'learning_rate': 2.4522585383767903e-07, 'epoch': 2.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234382629394531\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7215406894683838\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629620149482953), 'eval_cosine_accuracy_threshold': np.float32(0.72343826), 'eval_cosine_f1': np.float64(0.9630960572928614), 'eval_cosine_f1_threshold': np.float32(0.7215407), 'eval_cosine_precision': 0.9589656397502918, 'eval_cosine_recall': np.float64(0.9672622094809051), 'eval_cosine_ap': np.float64(0.9925803752087059), 'eval_runtime': 85.8688, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7353591918945312\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7224595546722412\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631027951264461), 'eval_cosine_accuracy_threshold': np.float32(0.7353592), 'eval_cosine_f1': np.float64(0.9632617741750761), 'eval_cosine_f1_threshold': np.float32(0.72245955), 'eval_cosine_precision': 0.9578134885486524, 'eval_cosine_recall': np.float64(0.968772396846524), 'eval_cosine_ap': np.float64(0.9926141908996202), 'eval_runtime': 85.8701, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.56}\n",
      "{'loss': 0.0047, 'grad_norm': 0.07527340203523636, 'learning_rate': 2.429305912596401e-07, 'epoch': 2.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7192283868789673\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7191976308822632\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632307771065834), 'eval_cosine_accuracy_threshold': np.float32(0.7192284), 'eval_cosine_f1': np.float64(0.9633129445416354), 'eval_cosine_f1_threshold': np.float32(0.71919763), 'eval_cosine_precision': 0.9611650485436893, 'eval_cosine_recall': np.float64(0.9654704617589843), 'eval_cosine_ap': np.float64(0.9927011295382044), 'eval_runtime': 86.3706, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7276871204376221\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7148493528366089\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628596293641856), 'eval_cosine_accuracy_threshold': np.float32(0.7276871), 'eval_cosine_f1': np.float64(0.9630053685469303), 'eval_cosine_f1_threshold': np.float32(0.71484935), 'eval_cosine_precision': 0.9572816025089788, 'eval_cosine_recall': np.float64(0.9687979932425514), 'eval_cosine_ap': np.float64(0.9926113337514892), 'eval_runtime': 85.9397, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.58}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7248528003692627\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7239166498184204\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629364185522679), 'eval_cosine_accuracy_threshold': np.float32(0.7248528), 'eval_cosine_f1': np.float64(0.9629837926274349), 'eval_cosine_f1_threshold': np.float32(0.72391665), 'eval_cosine_precision': 0.9617544934640523, 'eval_cosine_recall': np.float64(0.9642162383536398), 'eval_cosine_ap': np.float64(0.9925991601604993), 'eval_runtime': 85.8213, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.59}\n",
      "{'loss': 0.0045, 'grad_norm': 0.08161088824272156, 'learning_rate': 2.406353286816012e-07, 'epoch': 2.59}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7233158946037292\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7232556343078613\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628980239582267), 'eval_cosine_accuracy_threshold': np.float32(0.7233159), 'eval_cosine_f1': np.float64(0.9630073883139587), 'eval_cosine_f1_threshold': np.float32(0.72325563), 'eval_cosine_precision': 0.9601771037430978, 'eval_cosine_recall': np.float64(0.9658544076993959), 'eval_cosine_ap': np.float64(0.992622706178589), 'eval_runtime': 85.8394, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7155261635780334\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7155261635780334\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634099518787754), 'eval_cosine_accuracy_threshold': np.float32(0.71552616), 'eval_cosine_f1': np.float64(0.9635735854345305), 'eval_cosine_f1_threshold': np.float32(0.71552616), 'eval_cosine_precision': 0.9592835942058399, 'eval_cosine_recall': np.float64(0.9679021193815911), 'eval_cosine_ap': np.float64(0.992679054743271), 'eval_runtime': 85.8368, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.61}\n",
      "{'loss': 0.0045, 'grad_norm': 0.11023436486721039, 'learning_rate': 2.3834006610356225e-07, 'epoch': 2.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7196145057678223\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7140112519264221\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630388041363775), 'eval_cosine_accuracy_threshold': np.float32(0.7196145), 'eval_cosine_f1': np.float64(0.9632040686586141), 'eval_cosine_f1_threshold': np.float32(0.71401125), 'eval_cosine_precision': 0.9569501288464454, 'eval_cosine_recall': np.float64(0.9695402887273472), 'eval_cosine_ap': np.float64(0.9926110369939072), 'eval_runtime': 85.7749, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205767035484314\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205767035484314\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9625012798198014), 'eval_cosine_accuracy_threshold': np.float32(0.7205767), 'eval_cosine_f1': np.float64(0.9626941685765216), 'eval_cosine_f1_threshold': np.float32(0.7205767), 'eval_cosine_precision': 0.9577675314146737, 'eval_cosine_recall': np.float64(0.9676717518173441), 'eval_cosine_ap': np.float64(0.992492939477571), 'eval_runtime': 86.2965, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.63}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7148486375808716\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7110102772712708\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9622965086515819), 'eval_cosine_accuracy_threshold': np.float32(0.71484864), 'eval_cosine_f1': np.float64(0.962520341741253), 'eval_cosine_f1_threshold': np.float32(0.7110103), 'eval_cosine_precision': 0.9561988481357987, 'eval_cosine_recall': np.float64(0.9689259752226886), 'eval_cosine_ap': np.float64(0.9924674702486258), 'eval_runtime': 85.748, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.63}\n",
      "{'loss': 0.0044, 'grad_norm': 0.0722125768661499, 'learning_rate': 2.360448035255233e-07, 'epoch': 2.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7325378060340881\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7218763828277588\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9625012798198014), 'eval_cosine_accuracy_threshold': np.float32(0.7325378), 'eval_cosine_f1': np.float64(0.9626046559117828), 'eval_cosine_f1_threshold': np.float32(0.7218764), 'eval_cosine_precision': 0.9599582527237552, 'eval_cosine_recall': np.float64(0.9652656905907648), 'eval_cosine_ap': np.float64(0.9925025982453728), 'eval_runtime': 85.8782, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7317356467247009\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7154949903488159\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9625780690078837), 'eval_cosine_accuracy_threshold': np.float32(0.73173565), 'eval_cosine_f1': np.float64(0.9626590906195457), 'eval_cosine_f1_threshold': np.float32(0.715495), 'eval_cosine_precision': 0.9583005707038681, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9925364525755733), 'eval_runtime': 85.8155, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.65}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1271813064813614, 'learning_rate': 2.337495409474844e-07, 'epoch': 2.66}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7191683053970337\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7190418243408203\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9626164636019249), 'eval_cosine_accuracy_threshold': np.float32(0.7191683), 'eval_cosine_f1': np.float64(0.9626904752781289), 'eval_cosine_f1_threshold': np.float32(0.7190418), 'eval_cosine_precision': 0.9607883129796293, 'eval_cosine_recall': np.float64(0.9646001842940514), 'eval_cosine_ap': np.float64(0.9925675443808516), 'eval_runtime': 85.9452, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.66}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7268348336219788\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7268348336219788\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.72683483), 'eval_cosine_f1': np.float64(0.9629165494488633), 'eval_cosine_f1_threshold': np.float32(0.72683483), 'eval_cosine_precision': 0.9621045638064087, 'eval_cosine_recall': np.float64(0.9637299068291184), 'eval_cosine_ap': np.float64(0.9926141123931644), 'eval_runtime': 85.773, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.67}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7258296012878418\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7258296012878418\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628980239582267), 'eval_cosine_accuracy_threshold': np.float32(0.7258296), 'eval_cosine_f1': np.float64(0.9629572839600823), 'eval_cosine_f1_threshold': np.float32(0.7258296), 'eval_cosine_precision': 0.9614216824432934, 'eval_cosine_recall': np.float64(0.9644977987099417), 'eval_cosine_ap': np.float64(0.9925978872085627), 'eval_runtime': 86.3722, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.68}\n",
      "{'loss': 0.0046, 'grad_norm': 0.08046233654022217, 'learning_rate': 2.3145427836944544e-07, 'epoch': 2.69}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7290060520172119\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7263696193695068\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628596293641856), 'eval_cosine_accuracy_threshold': np.float32(0.72900605), 'eval_cosine_f1': np.float64(0.9628872303445456), 'eval_cosine_f1_threshold': np.float32(0.7263696), 'eval_cosine_precision': 0.9615109749872384, 'eval_cosine_recall': np.float64(0.9642674311456947), 'eval_cosine_ap': np.float64(0.9925832801167238), 'eval_runtime': 85.8867, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.69}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7206031084060669\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715477466583252\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628212347701444), 'eval_cosine_accuracy_threshold': np.float32(0.7206031), 'eval_cosine_f1': np.float64(0.9629261200596856), 'eval_cosine_f1_threshold': np.float32(0.71547747), 'eval_cosine_precision': 0.9595607859085479, 'eval_cosine_recall': np.float64(0.9663151428278899), 'eval_cosine_ap': np.float64(0.9926276472949418), 'eval_runtime': 85.812, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.7}\n",
      "{'loss': 0.0045, 'grad_norm': 0.08596356213092804, 'learning_rate': 2.2915901579140654e-07, 'epoch': 2.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7273417115211487\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173141241073608\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624244906317191), 'eval_cosine_accuracy_threshold': np.float32(0.7273417), 'eval_cosine_f1': np.float64(0.9625333044376172), 'eval_cosine_f1_threshold': np.float32(0.7173141), 'eval_cosine_precision': 0.9587809523809524, 'eval_cosine_recall': np.float64(0.9663151428278899), 'eval_cosine_ap': np.float64(0.9925281477035773), 'eval_runtime': 85.7968, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7271726131439209\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7271726131439209\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9623604996416505), 'eval_cosine_accuracy_threshold': np.float32(0.7271726), 'eval_cosine_f1': np.float64(0.9624340584245552), 'eval_cosine_f1_threshold': np.float32(0.7271726), 'eval_cosine_precision': 0.9605568445475638, 'eval_cosine_recall': np.float64(0.9643186239377496), 'eval_cosine_ap': np.float64(0.9924594599745202), 'eval_runtime': 85.7331, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7236322164535522\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7235002517700195\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9624244906317191), 'eval_cosine_accuracy_threshold': np.float32(0.7236322), 'eval_cosine_f1': np.float64(0.9625261653137285), 'eval_cosine_f1_threshold': np.float32(0.72350025), 'eval_cosine_precision': 0.959928716904277, 'eval_cosine_recall': np.float64(0.9651377086106276), 'eval_cosine_ap': np.float64(0.9924334036108022), 'eval_runtime': 85.9052, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.73}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1854192316532135, 'learning_rate': 2.268637532133676e-07, 'epoch': 2.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246227264404297\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7246227264404297\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628340329681581), 'eval_cosine_accuracy_threshold': np.float32(0.7246227), 'eval_cosine_f1': np.float64(0.962880589002224), 'eval_cosine_f1_threshold': np.float32(0.7246227), 'eval_cosine_precision': 0.9616759434203135, 'eval_cosine_recall': np.float64(0.9640882563735026), 'eval_cosine_ap': np.float64(0.9925674449187416), 'eval_runtime': 86.2758, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7210694551467896\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.716669499874115\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627316473840484), 'eval_cosine_accuracy_threshold': np.float32(0.72106946), 'eval_cosine_f1': np.float64(0.9628097590869048), 'eval_cosine_f1_threshold': np.float32(0.7166695), 'eval_cosine_precision': 0.9604676634657021, 'eval_cosine_recall': np.float64(0.965163305006655), 'eval_cosine_ap': np.float64(0.9925679399284042), 'eval_runtime': 85.8718, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.75}\n",
      "{'loss': 0.0047, 'grad_norm': 0.11945681273937225, 'learning_rate': 2.245684906353287e-07, 'epoch': 2.75}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7282060384750366\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7204458713531494\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630899969284324), 'eval_cosine_accuracy_threshold': np.float32(0.72820604), 'eval_cosine_f1': np.float64(0.9631311713826449), 'eval_cosine_f1_threshold': np.float32(0.7204459), 'eval_cosine_precision': 0.9600691795106567, 'eval_cosine_recall': np.float64(0.9662127572437801), 'eval_cosine_ap': np.float64(0.9926244185774374), 'eval_runtime': 85.8692, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.75}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7319052815437317\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7109500169754028\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627444455820621), 'eval_cosine_accuracy_threshold': np.float32(0.7319053), 'eval_cosine_f1': np.float64(0.9629893328925788), 'eval_cosine_f1_threshold': np.float32(0.71095), 'eval_cosine_precision': 0.9567007704938739, 'eval_cosine_recall': np.float64(0.9693611139551551), 'eval_cosine_ap': np.float64(0.9925726748364581), 'eval_runtime': 85.8789, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7265475988388062\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7066954970359802\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631283915224736), 'eval_cosine_accuracy_threshold': np.float32(0.7265476), 'eval_cosine_f1': np.float64(0.9631408423861254), 'eval_cosine_f1_threshold': np.float32(0.7066955), 'eval_cosine_precision': 0.9562277669853925, 'eval_cosine_recall': np.float64(0.9701546022320058), 'eval_cosine_ap': np.float64(0.9926756958852622), 'eval_runtime': 85.896, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.77}\n",
      "{'loss': 0.0045, 'grad_norm': 0.07522191852331161, 'learning_rate': 2.2227322805728974e-07, 'epoch': 2.78}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7308375835418701\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.728996753692627\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629492167502816), 'eval_cosine_accuracy_threshold': np.float32(0.7308376), 'eval_cosine_f1': np.float64(0.9629525357357664), 'eval_cosine_f1_threshold': np.float32(0.72899675), 'eval_cosine_precision': 0.9628662827895074, 'eval_cosine_recall': np.float64(0.9630388041363775), 'eval_cosine_ap': np.float64(0.9926438749318041), 'eval_runtime': 86.0041, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.78}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7249934077262878\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7224458456039429\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627444455820621), 'eval_cosine_accuracy_threshold': np.float32(0.7249934), 'eval_cosine_f1': np.float64(0.9628315421336124), 'eval_cosine_f1_threshold': np.float32(0.72244585), 'eval_cosine_precision': 0.9602576505932074, 'eval_cosine_recall': np.float64(0.9654192689669294), 'eval_cosine_ap': np.float64(0.992605008906587), 'eval_runtime': 86.373, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.79}\n",
      "{'loss': 0.0047, 'grad_norm': 0.047306887805461884, 'learning_rate': 2.199779654792508e-07, 'epoch': 2.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7197579145431519\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7197579145431519\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.7197579), 'eval_cosine_f1': np.float64(0.962961070808215), 'eval_cosine_f1_threshold': np.float32(0.7197579), 'eval_cosine_precision': 0.9609972468644845, 'eval_cosine_recall': np.float64(0.9649329374424082), 'eval_cosine_ap': np.float64(0.9926603808131651), 'eval_runtime': 85.8476, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7231864929199219\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7231001853942871\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631411897204873), 'eval_cosine_accuracy_threshold': np.float32(0.7231865), 'eval_cosine_f1': np.float64(0.9631788892298251), 'eval_cosine_f1_threshold': np.float32(0.7231002), 'eval_cosine_precision': 0.9621947481352815, 'eval_cosine_recall': np.float64(0.9641650455615849), 'eval_cosine_ap': np.float64(0.992674967616525), 'eval_runtime': 85.9021, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7300540208816528\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7300540208816528\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628212347701444), 'eval_cosine_accuracy_threshold': np.float32(0.730054), 'eval_cosine_f1': np.float64(0.9628312243305143), 'eval_cosine_f1_threshold': np.float32(0.730054), 'eval_cosine_precision': 0.9625725907544322, 'eval_cosine_recall': np.float64(0.9630899969284324), 'eval_cosine_ap': np.float64(0.9925697018988905), 'eval_runtime': 85.8701, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.82}\n",
      "{'loss': 0.0046, 'grad_norm': 0.06835879385471344, 'learning_rate': 2.1768270290121188e-07, 'epoch': 2.82}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724949836730957\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.713996946811676\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630132077403502), 'eval_cosine_accuracy_threshold': np.float32(0.72494984), 'eval_cosine_f1': np.float64(0.9630788804071246), 'eval_cosine_f1_threshold': np.float32(0.71399695), 'eval_cosine_precision': 0.957426894667611, 'eval_cosine_recall': np.float64(0.9687979932425514), 'eval_cosine_ap': np.float64(0.9926748168010607), 'eval_runtime': 85.9158, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.83}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7242032289505005\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7162419557571411\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631923825125422), 'eval_cosine_accuracy_threshold': np.float32(0.7242032), 'eval_cosine_f1': np.float64(0.9632811205567097), 'eval_cosine_f1_threshold': np.float32(0.71624196), 'eval_cosine_precision': 0.9593074911786358, 'eval_cosine_recall': np.float64(0.9672878058769325), 'eval_cosine_ap': np.float64(0.9927124777792959), 'eval_runtime': 85.8117, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.84}\n",
      "{'loss': 0.0044, 'grad_norm': 0.12846161425113678, 'learning_rate': 2.1538744032317296e-07, 'epoch': 2.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246404886245728\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7246404886245728\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633075662946657), 'eval_cosine_accuracy_threshold': np.float32(0.7246405), 'eval_cosine_f1': np.float64(0.9633014605173892), 'eval_cosine_f1_threshold': np.float32(0.7246405), 'eval_cosine_precision': 0.9634617846626552, 'eval_cosine_recall': np.float64(0.9631411897204873), 'eval_cosine_ap': np.float64(0.9927005166331421), 'eval_runtime': 86.3403, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.731941819190979\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7140976190567017\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630388041363775), 'eval_cosine_accuracy_threshold': np.float32(0.7319418), 'eval_cosine_f1': np.float64(0.9631692738140659), 'eval_cosine_f1_threshold': np.float32(0.7140976), 'eval_cosine_precision': 0.9571558566300996, 'eval_cosine_recall': np.float64(0.9692587283710453), 'eval_cosine_ap': np.float64(0.9926383710392847), 'eval_runtime': 85.8043, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234317064285278\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7234317064285278\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.96311559332446), 'eval_cosine_accuracy_threshold': np.float32(0.7234317), 'eval_cosine_f1': np.float64(0.96311559332446), 'eval_cosine_f1_threshold': np.float32(0.7234317), 'eval_cosine_precision': 0.96311559332446, 'eval_cosine_recall': np.float64(0.96311559332446), 'eval_cosine_ap': np.float64(0.9927013862070716), 'eval_runtime': 85.9362, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.86}\n",
      "{'loss': 0.0044, 'grad_norm': 0.04465008154511452, 'learning_rate': 2.1309217774513403e-07, 'epoch': 2.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7363444566726685\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.729808509349823\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629108221562404), 'eval_cosine_accuracy_threshold': np.float32(0.73634446), 'eval_cosine_f1': np.float64(0.9629269665076666), 'eval_cosine_f1_threshold': np.float32(0.7298085), 'eval_cosine_precision': 0.9621763909121113, 'eval_cosine_recall': np.float64(0.9636787140370636), 'eval_cosine_ap': np.float64(0.9926259399368809), 'eval_runtime': 85.8645, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7256002426147461\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7256002426147461\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632307771065834), 'eval_cosine_accuracy_threshold': np.float32(0.72560024), 'eval_cosine_f1': np.float64(0.9632303065207652), 'eval_cosine_f1_threshold': np.float32(0.72560024), 'eval_cosine_precision': 0.9632426344485115, 'eval_cosine_recall': np.float64(0.9632179789085696), 'eval_cosine_ap': np.float64(0.9926764116614515), 'eval_runtime': 85.9619, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.88}\n",
      "{'loss': 0.0044, 'grad_norm': 0.0490872748196125, 'learning_rate': 2.107969151670951e-07, 'epoch': 2.89}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7247339487075806\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7147485017776489\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631283915224736), 'eval_cosine_accuracy_threshold': np.float32(0.72473395), 'eval_cosine_f1': np.float64(0.9631623942529762), 'eval_cosine_f1_threshold': np.float32(0.7147485), 'eval_cosine_precision': 0.9602829300562298, 'eval_cosine_recall': np.float64(0.9660591788676154), 'eval_cosine_ap': np.float64(0.9926834011467973), 'eval_runtime': 86.3917, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.89}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7189960479736328\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7188520431518555\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628212347701444), 'eval_cosine_accuracy_threshold': np.float32(0.71899605), 'eval_cosine_f1': np.float64(0.9629175761753405), 'eval_cosine_f1_threshold': np.float32(0.71885204), 'eval_cosine_precision': 0.9604288151562221, 'eval_cosine_recall': np.float64(0.9654192689669294), 'eval_cosine_ap': np.float64(0.9926370391617928), 'eval_runtime': 86.0214, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7136681079864502\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7136681079864502\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632307771065834), 'eval_cosine_accuracy_threshold': np.float32(0.7136681), 'eval_cosine_f1': np.float64(0.9633223117284345), 'eval_cosine_f1_threshold': np.float32(0.7136681), 'eval_cosine_precision': 0.9609301377887579, 'eval_cosine_recall': np.float64(0.9657264257192587), 'eval_cosine_ap': np.float64(0.992720037411094), 'eval_runtime': 86.223, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.91}\n",
      "{'loss': 0.0044, 'grad_norm': 0.2469949722290039, 'learning_rate': 2.0850165258905618e-07, 'epoch': 2.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7292661666870117\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7089539766311646\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627572437800758), 'eval_cosine_accuracy_threshold': np.float32(0.72926617), 'eval_cosine_f1': np.float64(0.9628734594202714), 'eval_cosine_f1_threshold': np.float32(0.708954), 'eval_cosine_precision': 0.9569460245228163, 'eval_cosine_recall': np.float64(0.9688747824306337), 'eval_cosine_ap': np.float64(0.9926516155757169), 'eval_runtime': 86.2146, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228221893310547\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7227456569671631\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629620149482953), 'eval_cosine_accuracy_threshold': np.float32(0.7228222), 'eval_cosine_f1': np.float64(0.9630150291381249), 'eval_cosine_f1_threshold': np.float32(0.72274566), 'eval_cosine_precision': 0.9616385911179173, 'eval_cosine_recall': np.float64(0.9643954131258319), 'eval_cosine_ap': np.float64(0.992657552184465), 'eval_runtime': 86.2218, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.93}\n",
      "{'loss': 0.0044, 'grad_norm': 0.07677821815013885, 'learning_rate': 2.0620639001101725e-07, 'epoch': 2.94}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7387146949768066\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7254046201705933\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962795638374117), 'eval_cosine_accuracy_threshold': np.float32(0.7387147), 'eval_cosine_f1': np.float64(0.962813762859656), 'eval_cosine_f1_threshold': np.float32(0.7254046), 'eval_cosine_precision': 0.9590603174603175, 'eval_cosine_recall': np.float64(0.9665967031841917), 'eval_cosine_ap': np.float64(0.9925653397191581), 'eval_runtime': 86.1177, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.94}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7230839729309082\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7230839729309082\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9628212347701444), 'eval_cosine_accuracy_threshold': np.float32(0.723084), 'eval_cosine_f1': np.float64(0.9629336633789698), 'eval_cosine_f1_threshold': np.float32(0.723084), 'eval_cosine_precision': 0.9600305304668617, 'eval_cosine_recall': np.float64(0.9658544076993959), 'eval_cosine_ap': np.float64(0.992555402354782), 'eval_runtime': 86.6287, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7231196165084839\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7133747935295105\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629876113443228), 'eval_cosine_accuracy_threshold': np.float32(0.7231196), 'eval_cosine_f1': np.float64(0.9631494621194782), 'eval_cosine_f1_threshold': np.float32(0.7133748), 'eval_cosine_precision': 0.9569919644210846, 'eval_cosine_recall': np.float64(0.9693867103511825), 'eval_cosine_ap': np.float64(0.992605541858665), 'eval_runtime': 86.1375, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.96}\n",
      "{'loss': 0.0044, 'grad_norm': 0.07731997221708298, 'learning_rate': 2.0391112743297832e-07, 'epoch': 2.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7302279472351074\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173457145690918\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629108221562404), 'eval_cosine_accuracy_threshold': np.float32(0.73022795), 'eval_cosine_f1': np.float64(0.9629893691514418), 'eval_cosine_f1_threshold': np.float32(0.7173457), 'eval_cosine_precision': 0.9580008612609874, 'eval_cosine_recall': np.float64(0.9680301013617283), 'eval_cosine_ap': np.float64(0.9926106748681456), 'eval_runtime': 86.1544, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7232401371002197\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7174011468887329\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963614723046995), 'eval_cosine_accuracy_threshold': np.float32(0.72324014), 'eval_cosine_f1': np.float64(0.963704790457358), 'eval_cosine_f1_threshold': np.float32(0.71740115), 'eval_cosine_precision': 0.9606531525803088, 'eval_cosine_recall': np.float64(0.9667758779563838), 'eval_cosine_ap': np.float64(0.9927453307549533), 'eval_runtime': 86.1516, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.97}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1445976048707962, 'learning_rate': 2.016158648549394e-07, 'epoch': 2.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220842838287354\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144261598587036\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635379338589126), 'eval_cosine_accuracy_threshold': np.float32(0.7220843), 'eval_cosine_f1': np.float64(0.9635832919072566), 'eval_cosine_f1_threshold': np.float32(0.71442616), 'eval_cosine_precision': 0.9597054716262536, 'eval_cosine_recall': np.float64(0.967492577045152), 'eval_cosine_ap': np.float64(0.9927303565716123), 'eval_runtime': 86.1984, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225500345230103\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7225500345230103\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631283915224736), 'eval_cosine_accuracy_threshold': np.float32(0.72255003), 'eval_cosine_f1': np.float64(0.9631694003042583), 'eval_cosine_f1_threshold': np.float32(0.72255003), 'eval_cosine_precision': 0.9620993487421785, 'eval_cosine_recall': np.float64(0.9642418347496673), 'eval_cosine_ap': np.float64(0.9926693890780278), 'eval_runtime': 86.0533, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 2.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7180999517440796\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7180999517440796\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633331626906931), 'eval_cosine_accuracy_threshold': np.float32(0.71809995), 'eval_cosine_f1': np.float64(0.9634626911353984), 'eval_cosine_f1_threshold': np.float32(0.71809995), 'eval_cosine_precision': 0.9600711653323167, 'eval_cosine_recall': np.float64(0.9668782635404934), 'eval_cosine_ap': np.float64(0.9927133229841743), 'eval_runtime': 86.0326, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.0}\n",
      "{'loss': 0.0045, 'grad_norm': 0.0651516541838646, 'learning_rate': 1.9932060227690047e-07, 'epoch': 3.01}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7251028418540955\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7120534181594849\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627188491860346), 'eval_cosine_accuracy_threshold': np.float32(0.72510284), 'eval_cosine_f1': np.float64(0.9628601975358925), 'eval_cosine_f1_threshold': np.float32(0.7120534), 'eval_cosine_precision': 0.9575949367088608, 'eval_cosine_recall': np.float64(0.9681836797378929), 'eval_cosine_ap': np.float64(0.9926285281181778), 'eval_runtime': 86.0337, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.01}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7265908718109131\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7265908718109131\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629236203542542), 'eval_cosine_accuracy_threshold': np.float32(0.7265909), 'eval_cosine_f1': np.float64(0.9629941879031743), 'eval_cosine_f1_threshold': np.float32(0.7265909), 'eval_cosine_precision': 0.9611648009791672, 'eval_cosine_recall': np.float64(0.9648305518582984), 'eval_cosine_ap': np.float64(0.9926019365974088), 'eval_runtime': 86.0202, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.02}\n",
      "{'loss': 0.0043, 'grad_norm': 0.0732264295220375, 'learning_rate': 1.9702533969886154e-07, 'epoch': 3.03}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228119373321533\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7228119373321533\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630388041363775), 'eval_cosine_accuracy_threshold': np.float32(0.72281194), 'eval_cosine_f1': np.float64(0.9631350523359714), 'eval_cosine_f1_threshold': np.float32(0.72281194), 'eval_cosine_precision': 0.9606335302505602, 'eval_cosine_recall': np.float64(0.9656496365311764), 'eval_cosine_ap': np.float64(0.9926153762570868), 'eval_runtime': 85.8143, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.03}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7291451096534729\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7226035594940186\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963064400532405), 'eval_cosine_accuracy_threshold': np.float32(0.7291451), 'eval_cosine_f1': np.float64(0.9631368746092304), 'eval_cosine_f1_threshold': np.float32(0.72260356), 'eval_cosine_precision': 0.9602574867058494, 'eval_cosine_recall': np.float64(0.966033582471588), 'eval_cosine_ap': np.float64(0.9926252417872984), 'eval_runtime': 86.3982, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.04}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7326425909996033\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7099441885948181\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9627572437800758), 'eval_cosine_accuracy_threshold': np.float32(0.7326426), 'eval_cosine_f1': np.float64(0.9627731763929935), 'eval_cosine_f1_threshold': np.float32(0.7099442), 'eval_cosine_precision': 0.9542403137807055, 'eval_cosine_recall': np.float64(0.9714600184294051), 'eval_cosine_ap': np.float64(0.9926045032410729), 'eval_runtime': 85.8081, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.05}\n",
      "{'loss': 0.0045, 'grad_norm': 0.11373694241046906, 'learning_rate': 1.9473007712082262e-07, 'epoch': 3.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7302823066711426\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7133928537368774\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.7302823), 'eval_cosine_f1': np.float64(0.9630562232141722), 'eval_cosine_f1_threshold': np.float32(0.71339285), 'eval_cosine_precision': 0.9570074560849235, 'eval_cosine_recall': np.float64(0.969181939182963), 'eval_cosine_ap': np.float64(0.9926597305433114), 'eval_runtime': 85.9915, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7203360199928284\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7072612047195435\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632179789085696), 'eval_cosine_accuracy_threshold': np.float32(0.720336), 'eval_cosine_f1': np.float64(0.963315719106131), 'eval_cosine_f1_threshold': np.float32(0.7072612), 'eval_cosine_precision': 0.9584462969062761, 'eval_cosine_recall': np.float64(0.9682348725299478), 'eval_cosine_ap': np.float64(0.9927471378586147), 'eval_runtime': 85.929, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.07}\n",
      "{'loss': 0.0044, 'grad_norm': 0.14214254915714264, 'learning_rate': 1.924348145427837e-07, 'epoch': 3.08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237768173217773\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7114759683609009\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632435753045971), 'eval_cosine_accuracy_threshold': np.float32(0.7237768), 'eval_cosine_f1': np.float64(0.9633329086507835), 'eval_cosine_f1_threshold': np.float32(0.71147597), 'eval_cosine_precision': 0.9590076606970727, 'eval_cosine_recall': np.float64(0.9676973482133716), 'eval_cosine_ap': np.float64(0.9927649202166818), 'eval_runtime': 85.9902, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7182137370109558\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7118937969207764\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629620149482953), 'eval_cosine_accuracy_threshold': np.float32(0.71821374), 'eval_cosine_f1': np.float64(0.9631091698608157), 'eval_cosine_f1_threshold': np.float32(0.7118938), 'eval_cosine_precision': 0.9583132713311877, 'eval_cosine_recall': np.float64(0.967953312173646), 'eval_cosine_ap': np.float64(0.9927198447646404), 'eval_runtime': 85.986, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.08}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7150059938430786\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7149053812026978\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635507320569264), 'eval_cosine_accuracy_threshold': np.float32(0.715006), 'eval_cosine_f1': np.float64(0.9636215001021867), 'eval_cosine_f1_threshold': np.float32(0.7149054), 'eval_cosine_precision': 0.9617542070372259, 'eval_cosine_recall': np.float64(0.9654960581550118), 'eval_cosine_ap': np.float64(0.9928230801799653), 'eval_runtime': 86.3743, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.09}\n",
      "{'loss': 0.0044, 'grad_norm': 0.2258429080247879, 'learning_rate': 1.9013955196474476e-07, 'epoch': 3.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7230462431907654\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7230095267295837\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963153987918501), 'eval_cosine_accuracy_threshold': np.float32(0.72304624), 'eval_cosine_f1': np.float64(0.9632438367357361), 'eval_cosine_f1_threshold': np.float32(0.7230095), 'eval_cosine_precision': 0.9609006851932042, 'eval_cosine_recall': np.float64(0.9655984437391215), 'eval_cosine_ap': np.float64(0.9927398020475746), 'eval_runtime': 85.9153, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.1}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724058985710144\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7239635586738586\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634995392648715), 'eval_cosine_accuracy_threshold': np.float32(0.724059), 'eval_cosine_f1': np.float64(0.9635098134547966), 'eval_cosine_f1_threshold': np.float32(0.72396356), 'eval_cosine_precision': 0.9632386799693016, 'eval_cosine_recall': np.float64(0.9637810996211733), 'eval_cosine_ap': np.float64(0.9927865422142362), 'eval_runtime': 86.1201, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.11}\n",
      "{'loss': 0.0045, 'grad_norm': 0.07974524050951004, 'learning_rate': 1.878442893867058e-07, 'epoch': 3.12}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.727531909942627\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7111412286758423\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963153987918501), 'eval_cosine_accuracy_threshold': np.float32(0.7275319), 'eval_cosine_f1': np.float64(0.963283760271276), 'eval_cosine_f1_threshold': np.float32(0.7111412), 'eval_cosine_precision': 0.9559627939804896, 'eval_cosine_recall': np.float64(0.9707177229446095), 'eval_cosine_ap': np.float64(0.9927359233308725), 'eval_runtime': 85.8443, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.12}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7309730052947998\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7196642160415649\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.730973), 'eval_cosine_f1': np.float64(0.9636104174099179), 'eval_cosine_f1_threshold': np.float32(0.7196642), 'eval_cosine_precision': 0.9593566064542318, 'eval_cosine_recall': np.float64(0.9679021193815911), 'eval_cosine_ap': np.float64(0.9927768228429482), 'eval_runtime': 85.9237, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.13}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.721477210521698\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7204554080963135\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636019248489812), 'eval_cosine_accuracy_threshold': np.float32(0.7214772), 'eval_cosine_f1': np.float64(0.9636307833959947), 'eval_cosine_f1_threshold': np.float32(0.7204554), 'eval_cosine_precision': 0.9628673651929466, 'eval_cosine_recall': np.float64(0.9643954131258319), 'eval_cosine_ap': np.float64(0.9928069468478574), 'eval_runtime': 86.4359, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.14}\n",
      "{'loss': 0.0044, 'grad_norm': 0.16081705689430237, 'learning_rate': 1.855490268086669e-07, 'epoch': 3.14}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724243700504303\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7154141664505005\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635507320569264), 'eval_cosine_accuracy_threshold': np.float32(0.7242437), 'eval_cosine_f1': np.float64(0.9635686404571778), 'eval_cosine_f1_threshold': np.float32(0.71541417), 'eval_cosine_precision': 0.9604078726542237, 'eval_cosine_recall': np.float64(0.9667502815603563), 'eval_cosine_ap': np.float64(0.9928086775144587), 'eval_runtime': 85.9349, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.15}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7352952361106873\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.710201621055603\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632051807105559), 'eval_cosine_accuracy_threshold': np.float32(0.73529524), 'eval_cosine_f1': np.float64(0.9632394008631632), 'eval_cosine_f1_threshold': np.float32(0.7102016), 'eval_cosine_precision': 0.9554290894439967, 'eval_cosine_recall': np.float64(0.9711784580731033), 'eval_cosine_ap': np.float64(0.9927176055571535), 'eval_runtime': 86.104, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.16}\n",
      "{'loss': 0.0044, 'grad_norm': 0.14553917944431305, 'learning_rate': 1.8325376423062796e-07, 'epoch': 3.17}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7198889255523682\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.71981281042099\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.7198889), 'eval_cosine_f1': np.float64(0.964026934992447), 'eval_cosine_f1_threshold': np.float32(0.7198128), 'eval_cosine_precision': 0.964298519694719, 'eval_cosine_recall': np.float64(0.9637555032251459), 'eval_cosine_ap': np.float64(0.9928478279336267), 'eval_runtime': 85.9382, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.17}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7255823016166687\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7217093706130981\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633331626906931), 'eval_cosine_accuracy_threshold': np.float32(0.7255823), 'eval_cosine_f1': np.float64(0.9634259908915792), 'eval_cosine_f1_threshold': np.float32(0.7217094), 'eval_cosine_precision': 0.9603265430685893, 'eval_cosine_recall': np.float64(0.9665455103921368), 'eval_cosine_ap': np.float64(0.9927305055182637), 'eval_runtime': 86.0722, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7282670736312866\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7282670736312866\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634867410668578), 'eval_cosine_accuracy_threshold': np.float32(0.7282671), 'eval_cosine_f1': np.float64(0.9635114913862564), 'eval_cosine_f1_threshold': np.float32(0.7282671), 'eval_cosine_precision': 0.9628588226272334, 'eval_cosine_recall': np.float64(0.9641650455615849), 'eval_cosine_ap': np.float64(0.9927248689471889), 'eval_runtime': 85.9365, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.19}\n",
      "{'loss': 0.0043, 'grad_norm': 0.10469095408916473, 'learning_rate': 1.8095850165258906e-07, 'epoch': 3.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7373363971710205\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7287399768829346\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963384355482748), 'eval_cosine_accuracy_threshold': np.float32(0.7373364), 'eval_cosine_f1': np.float64(0.9633736602067019), 'eval_cosine_f1_threshold': np.float32(0.72874), 'eval_cosine_precision': 0.9616414598689077, 'eval_cosine_recall': np.float64(0.9651121122146001), 'eval_cosine_ap': np.float64(0.9927145159361679), 'eval_runtime': 86.4548, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7229739427566528\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.712758481502533\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638322924132282), 'eval_cosine_accuracy_threshold': np.float32(0.72297394), 'eval_cosine_f1': np.float64(0.9638600293199057), 'eval_cosine_f1_threshold': np.float32(0.7127585), 'eval_cosine_precision': 0.9600782182492318, 'eval_cosine_recall': np.float64(0.9676717518173441), 'eval_cosine_ap': np.float64(0.9928411306677188), 'eval_runtime': 85.928, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.2}\n",
      "{'loss': 0.0044, 'grad_norm': 0.12237625569105148, 'learning_rate': 1.786632390745501e-07, 'epoch': 3.21}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.729163408279419\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7196561098098755\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634867410668578), 'eval_cosine_accuracy_threshold': np.float32(0.7291634), 'eval_cosine_f1': np.float64(0.9635522239645083), 'eval_cosine_f1_threshold': np.float32(0.7196561), 'eval_cosine_precision': 0.9598201813425444, 'eval_cosine_recall': np.float64(0.9673134022729599), 'eval_cosine_ap': np.float64(0.9927691385819399), 'eval_runtime': 85.8849, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.21}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7279358506202698\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7262084484100342\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634867410668578), 'eval_cosine_accuracy_threshold': np.float32(0.72793585), 'eval_cosine_f1': np.float64(0.9635152243692214), 'eval_cosine_f1_threshold': np.float32(0.72620845), 'eval_cosine_precision': 0.9627641902425311, 'eval_cosine_recall': np.float64(0.9642674311456947), 'eval_cosine_ap': np.float64(0.9927300923786722), 'eval_runtime': 85.9791, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7308470010757446\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7308470010757446\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632691717006245), 'eval_cosine_accuracy_threshold': np.float32(0.730847), 'eval_cosine_f1': np.float64(0.9632211600071764), 'eval_cosine_f1_threshold': np.float32(0.730847), 'eval_cosine_precision': 0.9644818559770056, 'eval_cosine_recall': np.float64(0.9619637555032251), 'eval_cosine_ap': np.float64(0.9926816400774441), 'eval_runtime': 85.8928, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.23}\n",
      "{'loss': 0.0045, 'grad_norm': 0.0847858339548111, 'learning_rate': 1.763679764965112e-07, 'epoch': 3.24}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7318158149719238\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7318158149719238\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.962885225760213), 'eval_cosine_accuracy_threshold': np.float32(0.7318158), 'eval_cosine_f1': np.float64(0.9629298223188035), 'eval_cosine_f1_threshold': np.float32(0.7318158), 'eval_cosine_precision': 0.9617741688371381, 'eval_cosine_recall': np.float64(0.9640882563735026), 'eval_cosine_ap': np.float64(0.9925912407394768), 'eval_runtime': 85.879, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.24}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7281027436256409\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213436365127563\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632307771065834), 'eval_cosine_accuracy_threshold': np.float32(0.72810274), 'eval_cosine_f1': np.float64(0.9632764400708722), 'eval_cosine_f1_threshold': np.float32(0.72134364), 'eval_cosine_precision': 0.959424117004799, 'eval_cosine_recall': np.float64(0.9671598238967953), 'eval_cosine_ap': np.float64(0.9926866309441353), 'eval_runtime': 86.3641, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.25}\n",
      "{'loss': 0.0044, 'grad_norm': 0.09401978552341461, 'learning_rate': 1.7407271391847225e-07, 'epoch': 3.26}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7145953178405762\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145953178405762\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635507320569264), 'eval_cosine_accuracy_threshold': np.float32(0.7145953), 'eval_cosine_f1': np.float64(0.9636808814527648), 'eval_cosine_f1_threshold': np.float32(0.7145953), 'eval_cosine_precision': 0.9602521093829419, 'eval_cosine_recall': np.float64(0.9671342275007679), 'eval_cosine_ap': np.float64(0.9927913706303509), 'eval_runtime': 85.964, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.26}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7259324789047241\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7214558124542236\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631795843145285), 'eval_cosine_accuracy_threshold': np.float32(0.7259325), 'eval_cosine_f1': np.float64(0.9632616601799272), 'eval_cosine_f1_threshold': np.float32(0.7214558), 'eval_cosine_precision': 0.9604549965646233, 'eval_cosine_recall': np.float64(0.9660847752636429), 'eval_cosine_ap': np.float64(0.9927296365535458), 'eval_runtime': 85.8955, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.27}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7193639278411865\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7192907333374023\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632435753045971), 'eval_cosine_accuracy_threshold': np.float32(0.7193639), 'eval_cosine_f1': np.float64(0.9633962937472917), 'eval_cosine_f1_threshold': np.float32(0.71929073), 'eval_cosine_precision': 0.9594100624460578, 'eval_cosine_recall': np.float64(0.9674157878570697), 'eval_cosine_ap': np.float64(0.9927217971983932), 'eval_runtime': 85.9889, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.28}\n",
      "{'loss': 0.0045, 'grad_norm': 0.14596879482269287, 'learning_rate': 1.7177745134043333e-07, 'epoch': 3.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7181504368782043\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7116549015045166\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635635302549401), 'eval_cosine_accuracy_threshold': np.float32(0.71815044), 'eval_cosine_f1': np.float64(0.9636826690436777), 'eval_cosine_f1_threshold': np.float32(0.7116549), 'eval_cosine_precision': 0.9588718260605139, 'eval_cosine_recall': np.float64(0.9685420292822771), 'eval_cosine_ap': np.float64(0.992815121160824), 'eval_runtime': 85.8524, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.29}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7137327194213867\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7135748267173767\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632435753045971), 'eval_cosine_accuracy_threshold': np.float32(0.7137327), 'eval_cosine_f1': np.float64(0.9634298520386075), 'eval_cosine_f1_threshold': np.float32(0.7135748), 'eval_cosine_precision': 0.9585719353367456, 'eval_cosine_recall': np.float64(0.9683372581140576), 'eval_cosine_ap': np.float64(0.9927268663534008), 'eval_runtime': 85.9681, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.3}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1233275756239891, 'learning_rate': 1.6948218876239443e-07, 'epoch': 3.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.725191056728363\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7141627073287964\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963294768096652), 'eval_cosine_accuracy_threshold': np.float32(0.72519106), 'eval_cosine_f1': np.float64(0.963446774378366), 'eval_cosine_f1_threshold': np.float32(0.7141627), 'eval_cosine_precision': 0.9584800506649779, 'eval_cosine_recall': np.float64(0.9684652400941948), 'eval_cosine_ap': np.float64(0.9927334443684799), 'eval_runtime': 86.379, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225131988525391\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7224118709564209\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637810996211733), 'eval_cosine_accuracy_threshold': np.float32(0.7225132), 'eval_cosine_f1': np.float64(0.9637866612069407), 'eval_cosine_f1_threshold': np.float32(0.7224119), 'eval_cosine_precision': 0.9636386898669396, 'eval_cosine_recall': np.float64(0.963934677997338), 'eval_cosine_ap': np.float64(0.99281724108114), 'eval_runtime': 85.9612, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7214731574058533\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.719875693321228\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633075662946657), 'eval_cosine_accuracy_threshold': np.float32(0.72147316), 'eval_cosine_f1': np.float64(0.9634565887719476), 'eval_cosine_f1_threshold': np.float32(0.7198757), 'eval_cosine_precision': 0.9592276855939514, 'eval_cosine_recall': np.float64(0.967722944609399), 'eval_cosine_ap': np.float64(0.9927293437329561), 'eval_runtime': 86.0687, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.32}\n",
      "{'loss': 0.0044, 'grad_norm': 0.07837627083063126, 'learning_rate': 1.6718692618435547e-07, 'epoch': 3.33}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7178842425346375\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.704224169254303\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963704310433091), 'eval_cosine_accuracy_threshold': np.float32(0.71788424), 'eval_cosine_f1': np.float64(0.9637309654356483), 'eval_cosine_f1_threshold': np.float32(0.70422417), 'eval_cosine_precision': 0.9579908444826627, 'eval_cosine_recall': np.float64(0.9695402887273472), 'eval_cosine_ap': np.float64(0.9928174853062273), 'eval_runtime': 85.9176, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.33}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7173570394515991\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144200801849365\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9631027951264461), 'eval_cosine_accuracy_threshold': np.float32(0.71735704), 'eval_cosine_f1': np.float64(0.9632759015600744), 'eval_cosine_f1_threshold': np.float32(0.7144201), 'eval_cosine_precision': 0.9577913862037553, 'eval_cosine_recall': np.float64(0.9688235896385788), 'eval_cosine_ap': np.float64(0.9926935143724132), 'eval_runtime': 85.8328, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.34}\n",
      "{'loss': 0.0043, 'grad_norm': 0.1372937709093094, 'learning_rate': 1.6489166360631657e-07, 'epoch': 3.35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225157022476196\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145136594772339\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632307771065834), 'eval_cosine_accuracy_threshold': np.float32(0.7225157), 'eval_cosine_f1': np.float64(0.9634267389478507), 'eval_cosine_f1_threshold': np.float32(0.71451366), 'eval_cosine_precision': 0.9579895733157868, 'eval_cosine_recall': np.float64(0.9689259752226886), 'eval_cosine_ap': np.float64(0.9927407879045315), 'eval_runtime': 85.8658, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205623388290405\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7202638387680054\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9630388041363775), 'eval_cosine_accuracy_threshold': np.float32(0.72056234), 'eval_cosine_f1': np.float64(0.9631886200831059), 'eval_cosine_f1_threshold': np.float32(0.72026384), 'eval_cosine_precision': 0.9593002589752704, 'eval_cosine_recall': np.float64(0.9671086311047404), 'eval_cosine_ap': np.float64(0.9926878824064511), 'eval_runtime': 86.3854, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.36}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7319585084915161\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7318527698516846\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633587590867206), 'eval_cosine_accuracy_threshold': np.float32(0.7319585), 'eval_cosine_f1': np.float64(0.963378912495683), 'eval_cosine_f1_threshold': np.float32(0.73185277), 'eval_cosine_precision': 0.9628493262764951, 'eval_cosine_recall': np.float64(0.9639090816013105), 'eval_cosine_ap': np.float64(0.992708396134001), 'eval_runtime': 85.8644, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.37}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1401136964559555, 'learning_rate': 1.6259640102827762e-07, 'epoch': 3.37}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.719292402267456\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.719292402267456\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635507320569264), 'eval_cosine_accuracy_threshold': np.float32(0.7192924), 'eval_cosine_f1': np.float64(0.9636716158987704), 'eval_cosine_f1_threshold': np.float32(0.7192924), 'eval_cosine_precision': 0.960486167615948, 'eval_cosine_recall': np.float64(0.9668782635404934), 'eval_cosine_ap': np.float64(0.9928137438182417), 'eval_runtime': 85.9333, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.38}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246270179748535\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7180675268173218\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635379338589126), 'eval_cosine_accuracy_threshold': np.float32(0.724627), 'eval_cosine_f1': np.float64(0.9636542114455451), 'eval_cosine_f1_threshold': np.float32(0.7180675), 'eval_cosine_precision': 0.9599217738043837, 'eval_cosine_recall': np.float64(0.9674157878570697), 'eval_cosine_ap': np.float64(0.9928304180455019), 'eval_runtime': 85.812, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.39}\n",
      "{'loss': 0.0044, 'grad_norm': 0.11538111418485641, 'learning_rate': 1.6030113845023872e-07, 'epoch': 3.4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7249859571456909\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7238149642944336\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636787140370636), 'eval_cosine_accuracy_threshold': np.float32(0.72498596), 'eval_cosine_f1': np.float64(0.9637473813295181), 'eval_cosine_f1_threshold': np.float32(0.72381496), 'eval_cosine_precision': 0.9619288045695634, 'eval_cosine_recall': np.float64(0.9655728473430941), 'eval_cosine_ap': np.float64(0.9928415567760203), 'eval_runtime': 85.9084, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228888869285583\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.720029354095459\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963793897819187), 'eval_cosine_accuracy_threshold': np.float32(0.7228889), 'eval_cosine_f1': np.float64(0.9638563452619744), 'eval_cosine_f1_threshold': np.float32(0.72002935), 'eval_cosine_precision': 0.962196770655307, 'eval_cosine_recall': np.float64(0.9655216545510392), 'eval_cosine_ap': np.float64(0.9928784009348129), 'eval_runtime': 85.8531, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.725654125213623\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.725654125213623\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634099518787754), 'eval_cosine_accuracy_threshold': np.float32(0.7256541), 'eval_cosine_f1': np.float64(0.9634506475077663), 'eval_cosine_f1_threshold': np.float32(0.7256541), 'eval_cosine_precision': 0.9623802834886988, 'eval_cosine_recall': np.float64(0.9645233951059691), 'eval_cosine_ap': np.float64(0.9927470677386682), 'eval_runtime': 86.4333, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.42}\n",
      "{'loss': 0.0043, 'grad_norm': 0.08301497250795364, 'learning_rate': 1.5800587587219977e-07, 'epoch': 3.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228673696517944\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7228673696517944\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634483464728166), 'eval_cosine_accuracy_threshold': np.float32(0.72286737), 'eval_cosine_f1': np.float64(0.963531424777179), 'eval_cosine_f1_threshold': np.float32(0.72286737), 'eval_cosine_precision': 0.9613463792488407, 'eval_cosine_recall': np.float64(0.9657264257192587), 'eval_cosine_ap': np.float64(0.9927717125148017), 'eval_runtime': 85.9307, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7292913794517517\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7269717454910278\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633331626906931), 'eval_cosine_accuracy_threshold': np.float32(0.7292914), 'eval_cosine_f1': np.float64(0.9634065372829418), 'eval_cosine_f1_threshold': np.float32(0.72697175), 'eval_cosine_precision': 0.9611484765107511, 'eval_cosine_recall': np.float64(0.9656752329272038), 'eval_cosine_ap': np.float64(0.9927526053578454), 'eval_runtime': 85.7757, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.43}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1758735179901123, 'learning_rate': 1.5571061329416084e-07, 'epoch': 3.44}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7194708585739136\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.719386100769043\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636019248489812), 'eval_cosine_accuracy_threshold': np.float32(0.71947086), 'eval_cosine_f1': np.float64(0.9636763053029529), 'eval_cosine_f1_threshold': np.float32(0.7193861), 'eval_cosine_precision': 0.96171102273886, 'eval_cosine_recall': np.float64(0.9656496365311764), 'eval_cosine_ap': np.float64(0.9928299725730971), 'eval_runtime': 85.8258, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.44}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7245566844940186\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237243056297302\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.7245567), 'eval_cosine_f1': np.float64(0.9635687542328482), 'eval_cosine_f1_threshold': np.float32(0.7237243), 'eval_cosine_precision': 0.9620811962540509, 'eval_cosine_recall': np.float64(0.9650609194225453), 'eval_cosine_ap': np.float64(0.9927779520170419), 'eval_runtime': 85.7821, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724164605140686\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173254489898682\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634355482748029), 'eval_cosine_accuracy_threshold': np.float32(0.7241646), 'eval_cosine_f1': np.float64(0.9635717289094454), 'eval_cosine_f1_threshold': np.float32(0.71732545), 'eval_cosine_precision': 0.9593302042369656, 'eval_cosine_recall': np.float64(0.9678509265895362), 'eval_cosine_ap': np.float64(0.992769910357287), 'eval_runtime': 85.784, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.46}\n",
      "{'loss': 0.0044, 'grad_norm': 0.0802374854683876, 'learning_rate': 1.5341535071612191e-07, 'epoch': 3.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7232064604759216\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.718518078327179\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963793897819187), 'eval_cosine_accuracy_threshold': np.float32(0.72320646), 'eval_cosine_f1': np.float64(0.9638449549020108), 'eval_cosine_f1_threshold': np.float32(0.7185181), 'eval_cosine_precision': 0.9621486507167271, 'eval_cosine_recall': np.float64(0.9655472509470666), 'eval_cosine_ap': np.float64(0.992851324340977), 'eval_runtime': 86.3738, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7194211483001709\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7135311961174011\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639474761953517), 'eval_cosine_accuracy_threshold': np.float32(0.71942115), 'eval_cosine_f1': np.float64(0.9640227171207962), 'eval_cosine_f1_threshold': np.float32(0.7135312), 'eval_cosine_precision': 0.9613358108280092, 'eval_cosine_recall': np.float64(0.9667246851643289), 'eval_cosine_ap': np.float64(0.992870514545105), 'eval_runtime': 85.8196, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.48}\n",
      "{'loss': 0.0044, 'grad_norm': 0.11658290028572083, 'learning_rate': 1.51120088138083e-07, 'epoch': 3.49}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7146031260490417\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144663333892822\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634355482748029), 'eval_cosine_accuracy_threshold': np.float32(0.7146031), 'eval_cosine_f1': np.float64(0.9636305773025269), 'eval_cosine_f1_threshold': np.float32(0.71446633), 'eval_cosine_precision': 0.9585179932636058, 'eval_cosine_recall': np.float64(0.9687979932425514), 'eval_cosine_ap': np.float64(0.99274023175359), 'eval_runtime': 85.8371, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.49}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225396633148193\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.716818630695343\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.72253966), 'eval_cosine_f1': np.float64(0.9636064863763169), 'eval_cosine_f1_threshold': np.float32(0.71681863), 'eval_cosine_precision': 0.9591226068213516, 'eval_cosine_recall': np.float64(0.968132486945838), 'eval_cosine_ap': np.float64(0.992776070157386), 'eval_runtime': 85.9929, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7185317277908325\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7185317277908325\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963384355482748), 'eval_cosine_accuracy_threshold': np.float32(0.7185317), 'eval_cosine_f1': np.float64(0.9635703826319476), 'eval_cosine_f1_threshold': np.float32(0.7185317), 'eval_cosine_precision': 0.9586996731446525, 'eval_cosine_recall': np.float64(0.9684908364902222), 'eval_cosine_ap': np.float64(0.9927269746513391), 'eval_runtime': 85.8651, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.51}\n",
      "{'loss': 0.0045, 'grad_norm': 0.098497673869133, 'learning_rate': 1.4882482556004406e-07, 'epoch': 3.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7251179814338684\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7127402424812317\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637299068291184), 'eval_cosine_accuracy_threshold': np.float32(0.725118), 'eval_cosine_f1': np.float64(0.9639120873533413), 'eval_cosine_f1_threshold': np.float32(0.71274024), 'eval_cosine_precision': 0.9574253175424863, 'eval_cosine_recall': np.float64(0.9704873553803625), 'eval_cosine_ap': np.float64(0.9928006357110145), 'eval_runtime': 85.9375, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.52}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7174321413040161\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144691944122314\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638066960172008), 'eval_cosine_accuracy_threshold': np.float32(0.71743214), 'eval_cosine_f1': np.float64(0.9639652253605386), 'eval_cosine_f1_threshold': np.float32(0.7144692), 'eval_cosine_precision': 0.9587542726927459, 'eval_cosine_recall': np.float64(0.9692331319750179), 'eval_cosine_ap': np.float64(0.9928278696948151), 'eval_runtime': 86.4064, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.53}\n",
      "{'loss': 0.0043, 'grad_norm': 0.10947022587060928, 'learning_rate': 1.4652956298200513e-07, 'epoch': 3.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7226544618606567\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7100257277488708\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638578888092556), 'eval_cosine_accuracy_threshold': np.float32(0.72265446), 'eval_cosine_f1': np.float64(0.9640973501938108), 'eval_cosine_f1_threshold': np.float32(0.7100257), 'eval_cosine_precision': 0.9574172703637327, 'eval_cosine_recall': np.float64(0.970871301320774), 'eval_cosine_ap': np.float64(0.992834887599951), 'eval_runtime': 85.9742, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7177260518074036\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.711661696434021\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638322924132282), 'eval_cosine_accuracy_threshold': np.float32(0.71772605), 'eval_cosine_f1': np.float64(0.9640417710731503), 'eval_cosine_f1_threshold': np.float32(0.7116617), 'eval_cosine_precision': 0.958155293165786, 'eval_cosine_recall': np.float64(0.9700010238558411), 'eval_cosine_ap': np.float64(0.9928090395176713), 'eval_runtime': 85.9267, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.54}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218340635299683\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7217908501625061\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638450906112419), 'eval_cosine_accuracy_threshold': np.float32(0.72183406), 'eval_cosine_f1': np.float64(0.9640058609925463), 'eval_cosine_f1_threshold': np.float32(0.72179085), 'eval_cosine_precision': 0.9597381840322703, 'eval_cosine_recall': np.float64(0.9683116617180301), 'eval_cosine_ap': np.float64(0.992784545040979), 'eval_runtime': 85.8716, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.55}\n",
      "{'loss': 0.0045, 'grad_norm': 0.20591387152671814, 'learning_rate': 1.442343004039662e-07, 'epoch': 3.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7212675213813782\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7187259197235107\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7212675), 'eval_cosine_f1': np.float64(0.9641544702007808), 'eval_cosine_f1_threshold': np.float32(0.7187259), 'eval_cosine_precision': 0.957978521794062, 'eval_cosine_recall': np.float64(0.9704105661922802), 'eval_cosine_ap': np.float64(0.9928233179776592), 'eval_runtime': 85.9269, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.56}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7202057838439941\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7191826701164246\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638834852052831), 'eval_cosine_accuracy_threshold': np.float32(0.7202058), 'eval_cosine_f1': np.float64(0.964017035166909), 'eval_cosine_f1_threshold': np.float32(0.71918267), 'eval_cosine_precision': 0.9604654708064434, 'eval_cosine_recall': np.float64(0.9675949626292618), 'eval_cosine_ap': np.float64(0.9928671849039621), 'eval_runtime': 85.8248, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.57}\n",
      "{'loss': 0.0044, 'grad_norm': 0.08127636462450027, 'learning_rate': 1.4193903782592728e-07, 'epoch': 3.58}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7209683656692505\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145064473152161\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636659158390498), 'eval_cosine_accuracy_threshold': np.float32(0.72096837), 'eval_cosine_f1': np.float64(0.9638520515200448), 'eval_cosine_f1_threshold': np.float32(0.71450645), 'eval_cosine_precision': 0.9576059220332988, 'eval_cosine_recall': np.float64(0.9701801986280332), 'eval_cosine_ap': np.float64(0.9928178606254034), 'eval_runtime': 86.3912, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.58}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7238621711730957\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7210123538970947\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636403194430224), 'eval_cosine_accuracy_threshold': np.float32(0.7238622), 'eval_cosine_f1': np.float64(0.9637426005307206), 'eval_cosine_f1_threshold': np.float32(0.72101235), 'eval_cosine_precision': 0.9607030216705666, 'eval_cosine_recall': np.float64(0.9668014743524111), 'eval_cosine_ap': np.float64(0.9927984776381883), 'eval_runtime': 85.8261, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.59}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205852270126343\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205852270126343\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637683014231596), 'eval_cosine_accuracy_threshold': np.float32(0.7205852), 'eval_cosine_f1': np.float64(0.9638926088897393), 'eval_cosine_f1_threshold': np.float32(0.7205852), 'eval_cosine_precision': 0.960596893509927, 'eval_cosine_recall': np.float64(0.9672110166888502), 'eval_cosine_ap': np.float64(0.9928332175500387), 'eval_runtime': 85.8601, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.6}\n",
      "{'loss': 0.0043, 'grad_norm': 0.10216077417135239, 'learning_rate': 1.3964377524788833e-07, 'epoch': 3.6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7162773609161377\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7158417701721191\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638706870072694), 'eval_cosine_accuracy_threshold': np.float32(0.71627736), 'eval_cosine_f1': np.float64(0.9640624801089711), 'eval_cosine_f1_threshold': np.float32(0.71584177), 'eval_cosine_precision': 0.9589717614283906, 'eval_cosine_recall': np.float64(0.9692075355789905), 'eval_cosine_ap': np.float64(0.9928392009463203), 'eval_runtime': 85.774, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.61}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7188732028007507\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7188732028007507\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637171086311047), 'eval_cosine_accuracy_threshold': np.float32(0.7188732), 'eval_cosine_f1': np.float64(0.9639032837189168), 'eval_cosine_f1_threshold': np.float32(0.7188732), 'eval_cosine_precision': 0.9589825441463353, 'eval_cosine_recall': np.float64(0.9688747824306337), 'eval_cosine_ap': np.float64(0.9927989158284933), 'eval_runtime': 85.9116, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.62}\n",
      "{'loss': 0.0043, 'grad_norm': 0.12050781399011612, 'learning_rate': 1.3734851266984943e-07, 'epoch': 3.63}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205002307891846\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7203009724617004\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635635302549401), 'eval_cosine_accuracy_threshold': np.float32(0.72050023), 'eval_cosine_f1': np.float64(0.9637015031938088), 'eval_cosine_f1_threshold': np.float32(0.720301), 'eval_cosine_precision': 0.9600660485202591, 'eval_cosine_recall': np.float64(0.9673645950650148), 'eval_cosine_ap': np.float64(0.99276930536668), 'eval_runtime': 85.888, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.63}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7160201072692871\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7158631086349487\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963384355482748), 'eval_cosine_accuracy_threshold': np.float32(0.7160201), 'eval_cosine_f1': np.float64(0.9635889277760101), 'eval_cosine_f1_threshold': np.float32(0.7158631), 'eval_cosine_precision': 0.9582352494494647, 'eval_cosine_recall': np.float64(0.9690027644107709), 'eval_cosine_ap': np.float64(0.992725701182596), 'eval_runtime': 86.4071, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7245033979415894\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7134148478507996\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963294768096652), 'eval_cosine_accuracy_threshold': np.float32(0.7245034), 'eval_cosine_f1': np.float64(0.9635099581845219), 'eval_cosine_f1_threshold': np.float32(0.71341485), 'eval_cosine_precision': 0.956905909974502, 'eval_cosine_recall': np.float64(0.9702057950240606), 'eval_cosine_ap': np.float64(0.9927219047054577), 'eval_runtime': 85.8452, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.64}\n",
      "{'loss': 0.0043, 'grad_norm': 0.07938122749328613, 'learning_rate': 1.3505325009181048e-07, 'epoch': 3.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7242650985717773\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7096968293190002\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634099518787754), 'eval_cosine_accuracy_threshold': np.float32(0.7242651), 'eval_cosine_f1': np.float64(0.9634823254455156), 'eval_cosine_f1_threshold': np.float32(0.7096968), 'eval_cosine_precision': 0.9562794684954994, 'eval_cosine_recall': np.float64(0.9707945121326917), 'eval_cosine_ap': np.float64(0.9927423861764773), 'eval_runtime': 85.9075, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7171754837036133\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7167971134185791\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634611446708303), 'eval_cosine_accuracy_threshold': np.float32(0.7171755), 'eval_cosine_f1': np.float64(0.9635800028064446), 'eval_cosine_f1_threshold': np.float32(0.7167971), 'eval_cosine_precision': 0.9604557129415355, 'eval_cosine_recall': np.float64(0.9667246851643289), 'eval_cosine_ap': np.float64(0.9927821221623545), 'eval_runtime': 85.9327, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.66}\n",
      "{'loss': 0.0043, 'grad_norm': 0.09410575777292252, 'learning_rate': 1.3275798751377158e-07, 'epoch': 3.67}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7265599966049194\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7210496664047241\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634995392648715), 'eval_cosine_accuracy_threshold': np.float32(0.72656), 'eval_cosine_f1': np.float64(0.9636212942301561), 'eval_cosine_f1_threshold': np.float32(0.72104967), 'eval_cosine_precision': 0.9600833396854435, 'eval_cosine_recall': np.float64(0.9671854202928227), 'eval_cosine_ap': np.float64(0.9927979773069073), 'eval_runtime': 85.8685, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.67}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724229097366333\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7219960689544678\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963704310433091), 'eval_cosine_accuracy_threshold': np.float32(0.7242291), 'eval_cosine_f1': np.float64(0.9638010204081632), 'eval_cosine_f1_threshold': np.float32(0.72199607), 'eval_cosine_precision': 0.9605664598799959, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9928018495920811), 'eval_runtime': 85.9109, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.68}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722906231880188\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7226646542549133\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963614723046995), 'eval_cosine_accuracy_threshold': np.float32(0.72290623), 'eval_cosine_f1': np.float64(0.963712713952034), 'eval_cosine_f1_threshold': np.float32(0.72266465), 'eval_cosine_precision': 0.9611242648743603, 'eval_cosine_recall': np.float64(0.9663151428278899), 'eval_cosine_ap': np.float64(0.992803229373201), 'eval_runtime': 86.307, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.69}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1316576600074768, 'learning_rate': 1.3046272493573265e-07, 'epoch': 3.7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.719667375087738\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.719667375087738\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635251356608989), 'eval_cosine_accuracy_threshold': np.float32(0.7196674), 'eval_cosine_f1': np.float64(0.963623832133558), 'eval_cosine_f1_threshold': np.float32(0.7196674), 'eval_cosine_precision': 0.9610234215885947, 'eval_cosine_recall': np.float64(0.9662383536398075), 'eval_cosine_ap': np.float64(0.992792017507729), 'eval_runtime': 85.8379, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.7}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7197602987289429\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7197080850601196\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634995392648715), 'eval_cosine_accuracy_threshold': np.float32(0.7197603), 'eval_cosine_f1': np.float64(0.9635843611941061), 'eval_cosine_f1_threshold': np.float32(0.7197081), 'eval_cosine_precision': 0.9613503184713376, 'eval_cosine_recall': np.float64(0.9658288113033685), 'eval_cosine_ap': np.float64(0.9927755488536198), 'eval_runtime': 85.8732, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.71}\n",
      "{'loss': 0.0044, 'grad_norm': 0.06440592557191849, 'learning_rate': 1.2816746235769372e-07, 'epoch': 3.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7258546352386475\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7167864441871643\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632051807105559), 'eval_cosine_accuracy_threshold': np.float32(0.72585464), 'eval_cosine_f1': np.float64(0.9633898132600621), 'eval_cosine_f1_threshold': np.float32(0.71678644), 'eval_cosine_precision': 0.9575915435970058, 'eval_cosine_recall': np.float64(0.9692587283710453), 'eval_cosine_ap': np.float64(0.992714284395109), 'eval_runtime': 85.798, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.72}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7213583588600159\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213583588600159\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9629876113443228), 'eval_cosine_accuracy_threshold': np.float32(0.72135836), 'eval_cosine_f1': np.float64(0.9631376347923625), 'eval_cosine_f1_threshold': np.float32(0.72135836), 'eval_cosine_precision': 0.959249479510486, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9926646223124945), 'eval_runtime': 85.8186, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7184402942657471\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7111014127731323\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633459608887068), 'eval_cosine_accuracy_threshold': np.float32(0.7184403), 'eval_cosine_f1': np.float64(0.9634980795156819), 'eval_cosine_f1_threshold': np.float32(0.7111014), 'eval_cosine_precision': 0.9575307143940543, 'eval_cosine_recall': np.float64(0.9695402887273472), 'eval_cosine_ap': np.float64(0.9927640827898505), 'eval_runtime': 85.9587, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.74}\n",
      "{'loss': 0.0044, 'grad_norm': 0.06063104048371315, 'learning_rate': 1.258721997796548e-07, 'epoch': 3.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7145286798477173\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145286798477173\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9633715572847343), 'eval_cosine_accuracy_threshold': np.float32(0.7145287), 'eval_cosine_f1': np.float64(0.9635181644359465), 'eval_cosine_f1_threshold': np.float32(0.7145287), 'eval_cosine_precision': 0.9596770098014321, 'eval_cosine_recall': np.float64(0.9673901914610423), 'eval_cosine_ap': np.float64(0.9927832003895349), 'eval_runtime': 86.3144, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.75}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7212765216827393\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7185598611831665\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637427050271322), 'eval_cosine_accuracy_threshold': np.float32(0.7212765), 'eval_cosine_f1': np.float64(0.9638449659889992), 'eval_cosine_f1_threshold': np.float32(0.71855986), 'eval_cosine_precision': 0.9611341596884624, 'eval_cosine_recall': np.float64(0.9665711067881643), 'eval_cosine_ap': np.float64(0.9928313747180274), 'eval_runtime': 85.9629, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.75}\n",
      "{'loss': 0.0044, 'grad_norm': 0.07197641581296921, 'learning_rate': 1.2357693720161584e-07, 'epoch': 3.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218242287635803\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7191298007965088\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634355482748029), 'eval_cosine_accuracy_threshold': np.float32(0.7218242), 'eval_cosine_f1': np.float64(0.963551699942591), 'eval_cosine_f1_threshold': np.float32(0.7191298), 'eval_cosine_precision': 0.960500546837246, 'eval_cosine_recall': np.float64(0.9666222995802191), 'eval_cosine_ap': np.float64(0.9927956572103028), 'eval_runtime': 85.8861, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7137172222137451\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7136991024017334\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963793897819187), 'eval_cosine_accuracy_threshold': np.float32(0.7137172), 'eval_cosine_f1': np.float64(0.9639291588570554), 'eval_cosine_f1_threshold': np.float32(0.7136991), 'eval_cosine_precision': 0.960341454739463, 'eval_cosine_recall': np.float64(0.9675437698372069), 'eval_cosine_ap': np.float64(0.9928928111035147), 'eval_runtime': 85.7973, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218434810638428\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7218434810638428\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636915122350773), 'eval_cosine_accuracy_threshold': np.float32(0.7218435), 'eval_cosine_f1': np.float64(0.9637254024472888), 'eval_cosine_f1_threshold': np.float32(0.7218435), 'eval_cosine_precision': 0.9628267034567334, 'eval_cosine_recall': np.float64(0.9646257806900789), 'eval_cosine_ap': np.float64(0.9928655598282703), 'eval_runtime': 85.8387, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.78}\n",
      "{'loss': 0.0046, 'grad_norm': 0.18971703946590424, 'learning_rate': 1.2128167462357692e-07, 'epoch': 3.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7278201580047607\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7207491397857666\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.72782016), 'eval_cosine_f1': np.float64(0.9635392090597222), 'eval_cosine_f1_threshold': np.float32(0.72074914), 'eval_cosine_precision': 0.960147413902656, 'eval_cosine_recall': np.float64(0.9669550527285758), 'eval_cosine_ap': np.float64(0.9928040566629125), 'eval_runtime': 85.7694, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.79}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7232980728149414\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7222744822502136\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636915122350773), 'eval_cosine_accuracy_threshold': np.float32(0.7232981), 'eval_cosine_f1': np.float64(0.9637402384938841), 'eval_cosine_f1_threshold': np.float32(0.7222745), 'eval_cosine_precision': 0.9624486253286703, 'eval_cosine_recall': np.float64(0.9650353230265178), 'eval_cosine_ap': np.float64(0.9928456412854118), 'eval_runtime': 86.2721, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.8}\n",
      "{'loss': 0.0045, 'grad_norm': 0.18148332834243774, 'learning_rate': 1.18986412045538e-07, 'epoch': 3.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7171001434326172\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7171001434326172\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638322924132282), 'eval_cosine_accuracy_threshold': np.float32(0.71710014), 'eval_cosine_f1': np.float64(0.9639632746748278), 'eval_cosine_f1_threshold': np.float32(0.71710014), 'eval_cosine_precision': 0.9604848546452531, 'eval_cosine_recall': np.float64(0.9674669806491246), 'eval_cosine_ap': np.float64(0.9928736519756122), 'eval_runtime': 85.8001, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.81}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7193241119384766\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7193241119384766\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637810996211733), 'eval_cosine_accuracy_threshold': np.float32(0.7193241), 'eval_cosine_f1': np.float64(0.9638956929985711), 'eval_cosine_f1_threshold': np.float32(0.7193241), 'eval_cosine_precision': 0.9608556312951471, 'eval_cosine_recall': np.float64(0.9669550527285758), 'eval_cosine_ap': np.float64(0.9928439975873764), 'eval_runtime': 85.8174, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.82}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7193092107772827\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.718472957611084\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638962834032968), 'eval_cosine_accuracy_threshold': np.float32(0.7193092), 'eval_cosine_f1': np.float64(0.9640100532003113), 'eval_cosine_f1_threshold': np.float32(0.71847296), 'eval_cosine_precision': 0.9609818135571665, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9928915213058319), 'eval_runtime': 85.7749, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.83}\n",
      "{'loss': 0.0044, 'grad_norm': 0.11933622509241104, 'learning_rate': 1.1669114946749906e-07, 'epoch': 3.83}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.715124249458313\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715124249458313\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.71512425), 'eval_cosine_f1': np.float64(0.964176440591535), 'eval_cosine_f1_threshold': np.float32(0.71512425), 'eval_cosine_precision': 0.9604541298384639, 'eval_cosine_recall': np.float64(0.9679277157776185), 'eval_cosine_ap': np.float64(0.9929216994708874), 'eval_runtime': 85.9495, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7179391384124756\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7179391384124756\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.71793914), 'eval_cosine_f1': np.float64(0.9640741497293434), 'eval_cosine_f1_threshold': np.float32(0.71793914), 'eval_cosine_precision': 0.9617167600611309, 'eval_cosine_recall': np.float64(0.9664431248080271), 'eval_cosine_ap': np.float64(0.9928980424806284), 'eval_runtime': 85.7743, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.85}\n",
      "{'loss': 0.0042, 'grad_norm': 0.09777046740055084, 'learning_rate': 1.1439588688946015e-07, 'epoch': 3.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216740846633911\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715227484703064\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637171086311047), 'eval_cosine_accuracy_threshold': np.float32(0.7216741), 'eval_cosine_f1': np.float64(0.9638897727995926), 'eval_cosine_f1_threshold': np.float32(0.7152275), 'eval_cosine_precision': 0.9586550877281819, 'eval_cosine_recall': np.float64(0.969181939182963), 'eval_cosine_ap': np.float64(0.9928458044341574), 'eval_runtime': 86.3696, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.86}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.717134952545166\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7149432897567749\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638194942152145), 'eval_cosine_accuracy_threshold': np.float32(0.71713495), 'eval_cosine_f1': np.float64(0.9639721156664586), 'eval_cosine_f1_threshold': np.float32(0.7149433), 'eval_cosine_precision': 0.9599228406812356, 'eval_cosine_recall': np.float64(0.9680556977577557), 'eval_cosine_ap': np.float64(0.9928764296199337), 'eval_runtime': 85.8296, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237921953201294\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7209944725036621\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637683014231596), 'eval_cosine_accuracy_threshold': np.float32(0.7237922), 'eval_cosine_f1': np.float64(0.9638495795424095), 'eval_cosine_f1_threshold': np.float32(0.7209945), 'eval_cosine_precision': 0.9610168197664063, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.992839981177213), 'eval_runtime': 85.8399, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.87}\n",
      "{'loss': 0.0043, 'grad_norm': 0.05500585958361626, 'learning_rate': 1.1210062431142122e-07, 'epoch': 3.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7199715375900269\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7181804180145264\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638450906112419), 'eval_cosine_accuracy_threshold': np.float32(0.71997154), 'eval_cosine_f1': np.float64(0.9639270629968483), 'eval_cosine_f1_threshold': np.float32(0.7181804), 'eval_cosine_precision': 0.9610696928831327, 'eval_cosine_recall': np.float64(0.9668014743524111), 'eval_cosine_ap': np.float64(0.992862922307615), 'eval_runtime': 85.8929, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.88}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7166340351104736\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7165216207504272\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963704310433091), 'eval_cosine_accuracy_threshold': np.float32(0.71663404), 'eval_cosine_f1': np.float64(0.9638246849329047), 'eval_cosine_f1_threshold': np.float32(0.7165216), 'eval_cosine_precision': 0.9606387306753458, 'eval_cosine_recall': np.float64(0.9670318419166581), 'eval_cosine_ap': np.float64(0.9928446370316318), 'eval_runtime': 85.7991, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.89}\n",
      "{'loss': 0.0044, 'grad_norm': 0.2138383388519287, 'learning_rate': 1.098053617333823e-07, 'epoch': 3.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7270917296409607\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.711965799331665\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634099518787754), 'eval_cosine_accuracy_threshold': np.float32(0.7270917), 'eval_cosine_f1': np.float64(0.9635829400765737), 'eval_cosine_f1_threshold': np.float32(0.7119658), 'eval_cosine_precision': 0.9577233305519735, 'eval_cosine_recall': np.float64(0.9695146923313197), 'eval_cosine_ap': np.float64(0.9928092314602085), 'eval_runtime': 86.3162, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.9}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7119054198265076\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7118629217147827\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638322924132282), 'eval_cosine_accuracy_threshold': np.float32(0.7119054), 'eval_cosine_f1': np.float64(0.9639605172545719), 'eval_cosine_f1_threshold': np.float32(0.7118629), 'eval_cosine_precision': 0.9605550754841661, 'eval_cosine_recall': np.float64(0.9673901914610423), 'eval_cosine_ap': np.float64(0.9928841074606533), 'eval_runtime': 85.7789, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7164214849472046\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7088145017623901\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9632819698986382), 'eval_cosine_accuracy_threshold': np.float32(0.7164215), 'eval_cosine_f1': np.float64(0.9635201585748593), 'eval_cosine_f1_threshold': np.float32(0.7088145), 'eval_cosine_precision': 0.9566522847122347, 'eval_cosine_recall': np.float64(0.9704873553803625), 'eval_cosine_ap': np.float64(0.9927891099124625), 'eval_runtime': 85.7607, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.92}\n",
      "{'loss': 0.0042, 'grad_norm': 0.14555983245372772, 'learning_rate': 1.0751009915534337e-07, 'epoch': 3.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7160758972167969\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7143727540969849\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963614723046995), 'eval_cosine_accuracy_threshold': np.float32(0.7160759), 'eval_cosine_f1': np.float64(0.9637626664967178), 'eval_cosine_f1_threshold': np.float32(0.71437275), 'eval_cosine_precision': 0.9598598522355092, 'eval_cosine_recall': np.float64(0.9676973482133716), 'eval_cosine_ap': np.float64(0.9928427567591499), 'eval_runtime': 85.7915, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7224611043930054\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7224611043930054\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634611446708303), 'eval_cosine_accuracy_threshold': np.float32(0.7224611), 'eval_cosine_f1': np.float64(0.9635995053102648), 'eval_cosine_f1_threshold': np.float32(0.7224611), 'eval_cosine_precision': 0.9599644354121681, 'eval_cosine_recall': np.float64(0.9672622094809051), 'eval_cosine_ap': np.float64(0.9927723916061509), 'eval_runtime': 85.889, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.94}\n",
      "{'loss': 0.0044, 'grad_norm': 0.11717958003282547, 'learning_rate': 1.0521483657730444e-07, 'epoch': 3.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.724129319190979\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.724129319190979\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9634227500767892), 'eval_cosine_accuracy_threshold': np.float32(0.7241293), 'eval_cosine_f1': np.float64(0.9635487080070402), 'eval_cosine_f1_threshold': np.float32(0.7241293), 'eval_cosine_precision': 0.9602420051858254, 'eval_cosine_recall': np.float64(0.9668782635404934), 'eval_cosine_ap': np.float64(0.9927378446229964), 'eval_runtime': 85.7643, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.95}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7233192324638367\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7223122119903564\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636659158390498), 'eval_cosine_accuracy_threshold': np.float32(0.72331923), 'eval_cosine_f1': np.float64(0.9637545163225962), 'eval_cosine_f1_threshold': np.float32(0.7223122), 'eval_cosine_precision': 0.9614101225196771, 'eval_cosine_recall': np.float64(0.9661103716596703), 'eval_cosine_ap': np.float64(0.9927906278910961), 'eval_runtime': 86.3673, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7202085256576538\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7202085256576538\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637810996211733), 'eval_cosine_accuracy_threshold': np.float32(0.7202085), 'eval_cosine_f1': np.float64(0.9638938504720591), 'eval_cosine_f1_threshold': np.float32(0.7202085), 'eval_cosine_precision': 0.9609025234025234, 'eval_cosine_recall': np.float64(0.966903859936521), 'eval_cosine_ap': np.float64(0.9928275461245392), 'eval_runtime': 85.8374, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.97}\n",
      "{'loss': 0.0043, 'grad_norm': 0.12556803226470947, 'learning_rate': 1.0291957399926552e-07, 'epoch': 3.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7200048565864563\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7200048565864563\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640498617794615), 'eval_cosine_accuracy_threshold': np.float32(0.72000486), 'eval_cosine_f1': np.float64(0.9641649763353617), 'eval_cosine_f1_threshold': np.float32(0.72000486), 'eval_cosine_precision': 0.9610875149418856, 'eval_cosine_recall': np.float64(0.9672622094809051), 'eval_cosine_ap': np.float64(0.9928769315542982), 'eval_runtime': 85.8481, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205651998519897\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205586433410645\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.7205652), 'eval_cosine_f1': np.float64(0.9641020405558889), 'eval_cosine_f1_threshold': np.float32(0.72055864), 'eval_cosine_precision': 0.9613417148092536, 'eval_cosine_recall': np.float64(0.9668782635404934), 'eval_cosine_ap': np.float64(0.9928677998937124), 'eval_runtime': 85.9654, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.98}\n",
      "{'loss': 0.0044, 'grad_norm': 0.07456537336111069, 'learning_rate': 1.0062431142122659e-07, 'epoch': 3.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.716229259967804\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7150963544845581\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638450906112419), 'eval_cosine_accuracy_threshold': np.float32(0.71622926), 'eval_cosine_f1': np.float64(0.963996686420697), 'eval_cosine_f1_threshold': np.float32(0.71509635), 'eval_cosine_precision': 0.9599715714394497, 'eval_cosine_recall': np.float64(0.9680556977577557), 'eval_cosine_ap': np.float64(0.9928681819504723), 'eval_runtime': 85.8306, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 3.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225092053413391\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.715609610080719\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638834852052831), 'eval_cosine_accuracy_threshold': np.float32(0.7225092), 'eval_cosine_f1': np.float64(0.9640230925102272), 'eval_cosine_f1_threshold': np.float32(0.7156096), 'eval_cosine_precision': 0.9599736033909491, 'eval_cosine_recall': np.float64(0.9681068905498106), 'eval_cosine_ap': np.float64(0.9928852425535384), 'eval_runtime': 85.8063, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7173011898994446\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173011898994446\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963793897819187), 'eval_cosine_accuracy_threshold': np.float32(0.7173012), 'eval_cosine_f1': np.float64(0.9638987787603843), 'eval_cosine_f1_threshold': np.float32(0.7173012), 'eval_cosine_precision': 0.9611146456292149, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9928594916343992), 'eval_runtime': 86.005, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.01}\n",
      "{'loss': 0.0042, 'grad_norm': 0.12424352020025253, 'learning_rate': 9.832904884318765e-08, 'epoch': 4.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.72821444272995\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7234604358673096\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.72821444), 'eval_cosine_f1': np.float64(0.9635929458938309), 'eval_cosine_f1_threshold': np.float32(0.72346044), 'eval_cosine_precision': 0.9614688718432252, 'eval_cosine_recall': np.float64(0.9657264257192587), 'eval_cosine_ap': np.float64(0.9927898670602278), 'eval_runtime': 85.8234, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.02}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7225501537322998\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7225501537322998\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637683014231596), 'eval_cosine_accuracy_threshold': np.float32(0.72255015), 'eval_cosine_f1': np.float64(0.9638011942664979), 'eval_cosine_f1_threshold': np.float32(0.72255015), 'eval_cosine_precision': 0.9629270037558445, 'eval_cosine_recall': np.float64(0.9646769734821338), 'eval_cosine_ap': np.float64(0.9928270818010165), 'eval_runtime': 85.8964, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.03}\n",
      "{'loss': 0.0042, 'grad_norm': 0.09613987803459167, 'learning_rate': 9.603378626514872e-08, 'epoch': 4.04}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.717798113822937\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173957824707031\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637171086311047), 'eval_cosine_accuracy_threshold': np.float32(0.7177981), 'eval_cosine_f1': np.float64(0.9638434363402161), 'eval_cosine_f1_threshold': np.float32(0.7173958), 'eval_cosine_precision': 0.9604992247273837, 'eval_cosine_recall': np.float64(0.9672110166888502), 'eval_cosine_ap': np.float64(0.9928277317084291), 'eval_runtime': 85.8179, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.04}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7277770042419434\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7174715995788574\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635251356608989), 'eval_cosine_accuracy_threshold': np.float32(0.727777), 'eval_cosine_f1': np.float64(0.9636764387366383), 'eval_cosine_f1_threshold': np.float32(0.7174716), 'eval_cosine_precision': 0.9593617614976789, 'eval_cosine_recall': np.float64(0.9680301013617283), 'eval_cosine_ap': np.float64(0.9927979166309782), 'eval_runtime': 86.3905, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.05}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7209617495536804\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7208397388458252\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635251356608989), 'eval_cosine_accuracy_threshold': np.float32(0.72096175), 'eval_cosine_f1': np.float64(0.9636479591836735), 'eval_cosine_f1_threshold': np.float32(0.72083974), 'eval_cosine_precision': 0.9604139123360114, 'eval_cosine_recall': np.float64(0.966903859936521), 'eval_cosine_ap': np.float64(0.9928015675685177), 'eval_runtime': 85.8618, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.06}\n",
      "{'loss': 0.0045, 'grad_norm': 0.08346104621887207, 'learning_rate': 9.37385236871098e-08, 'epoch': 4.06}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7159634828567505\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7159634828567505\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7159635), 'eval_cosine_f1': np.float64(0.9640587109125718), 'eval_cosine_f1_threshold': np.float32(0.7159635), 'eval_cosine_precision': 0.9614327172750878, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9928787463148545), 'eval_runtime': 85.7488, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.07}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7164415121078491\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7163328528404236\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636915122350773), 'eval_cosine_accuracy_threshold': np.float32(0.7164415), 'eval_cosine_f1': np.float64(0.9638354557854347), 'eval_cosine_f1_threshold': np.float32(0.71633285), 'eval_cosine_precision': 0.9600294573249701, 'eval_cosine_recall': np.float64(0.9676717518173441), 'eval_cosine_ap': np.float64(0.9928717846686846), 'eval_runtime': 85.7724, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.08}\n",
      "{'loss': 0.0044, 'grad_norm': 0.15828678011894226, 'learning_rate': 9.144326110907087e-08, 'epoch': 4.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7181109189987183\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7175930738449097\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9635123374628852), 'eval_cosine_accuracy_threshold': np.float32(0.7181109), 'eval_cosine_f1': np.float64(0.9636727360762478), 'eval_cosine_f1_threshold': np.float32(0.7175931), 'eval_cosine_precision': 0.9594550021566488, 'eval_cosine_recall': np.float64(0.9679277157776185), 'eval_cosine_ap': np.float64(0.9928277379363305), 'eval_runtime': 85.6654, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7190316319465637\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7190316319465637\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.71903163), 'eval_cosine_f1': np.float64(0.964067190394073), 'eval_cosine_f1_threshold': np.float32(0.71903163), 'eval_cosine_precision': 0.962235765101869, 'eval_cosine_recall': np.float64(0.9659056004914508), 'eval_cosine_ap': np.float64(0.9929103953137749), 'eval_runtime': 86.1965, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.09}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246773838996887\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7172993421554565\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637555032251459), 'eval_cosine_accuracy_threshold': np.float32(0.7246774), 'eval_cosine_f1': np.float64(0.9638388420274714), 'eval_cosine_f1_threshold': np.float32(0.71729934), 'eval_cosine_precision': 0.9596082610240017, 'eval_cosine_recall': np.float64(0.9681068905498106), 'eval_cosine_ap': np.float64(0.9928685718533825), 'eval_runtime': 85.6885, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.1}\n",
      "{'loss': 0.0043, 'grad_norm': 0.10413317382335663, 'learning_rate': 8.914799853103195e-08, 'epoch': 4.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7235704064369202\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7202569842338562\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.7235704), 'eval_cosine_f1': np.float64(0.9640859281207693), 'eval_cosine_f1_threshold': np.float32(0.720257), 'eval_cosine_precision': 0.962095335202651, 'eval_cosine_recall': np.float64(0.9660847752636429), 'eval_cosine_ap': np.float64(0.992918478772734), 'eval_runtime': 85.8496, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.11}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7254228591918945\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.722947359085083\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638450906112419), 'eval_cosine_accuracy_threshold': np.float32(0.72542286), 'eval_cosine_f1': np.float64(0.963923380252932), 'eval_cosine_f1_threshold': np.float32(0.72294736), 'eval_cosine_precision': 0.9611635660295726, 'eval_cosine_recall': np.float64(0.9666990887683015), 'eval_cosine_ap': np.float64(0.9928667905720531), 'eval_runtime': 85.7578, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.12}\n",
      "{'loss': 0.0043, 'grad_norm': 0.06558360904455185, 'learning_rate': 8.685273595299302e-08, 'epoch': 4.13}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7253240346908569\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7246644496917725\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638962834032968), 'eval_cosine_accuracy_threshold': np.float32(0.72532403), 'eval_cosine_f1': np.float64(0.9640036238818921), 'eval_cosine_f1_threshold': np.float32(0.72466445), 'eval_cosine_precision': 0.961146026818656, 'eval_cosine_recall': np.float64(0.9668782635404934), 'eval_cosine_ap': np.float64(0.9928625323882297), 'eval_runtime': 85.851, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.13}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7196478843688965\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7196478843688965\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641138527695301), 'eval_cosine_accuracy_threshold': np.float32(0.7196479), 'eval_cosine_f1': np.float64(0.9641899312916655), 'eval_cosine_f1_threshold': np.float32(0.7196479), 'eval_cosine_precision': 0.9621501758678697, 'eval_cosine_recall': np.float64(0.9662383536398075), 'eval_cosine_ap': np.float64(0.9929033236478862), 'eval_runtime': 85.7753, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.14}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218220233917236\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7215917706489563\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640882563735026), 'eval_cosine_accuracy_threshold': np.float32(0.721822), 'eval_cosine_f1': np.float64(0.9641488219962181), 'eval_cosine_f1_threshold': np.float32(0.7215918), 'eval_cosine_precision': 0.9625255102040816, 'eval_cosine_recall': np.float64(0.9657776185113136), 'eval_cosine_ap': np.float64(0.9929056252407564), 'eval_runtime': 86.3129, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.15}\n",
      "{'loss': 0.0043, 'grad_norm': 0.1031024381518364, 'learning_rate': 8.455747337495409e-08, 'epoch': 4.15}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7227253913879395\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7221826314926147\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638450906112419), 'eval_cosine_accuracy_threshold': np.float32(0.7227254), 'eval_cosine_f1': np.float64(0.963922199660294), 'eval_cosine_f1_threshold': np.float32(0.72218263), 'eval_cosine_precision': 0.961870778641519, 'eval_cosine_recall': np.float64(0.9659823896795331), 'eval_cosine_ap': np.float64(0.9928696093056466), 'eval_runtime': 85.7541, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.16}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7167303562164307\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7167303562164307\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642290365516535), 'eval_cosine_accuracy_threshold': np.float32(0.71673036), 'eval_cosine_f1': np.float64(0.9643025914147414), 'eval_cosine_f1_threshold': np.float32(0.71673036), 'eval_cosine_precision': 0.9623237910729308, 'eval_cosine_recall': np.float64(0.9662895464318624), 'eval_cosine_ap': np.float64(0.9929585774152663), 'eval_runtime': 85.7714, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.17}\n",
      "{'loss': 0.0043, 'grad_norm': 0.09005077928304672, 'learning_rate': 8.226221079691515e-08, 'epoch': 4.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220784425735474\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7220784425735474\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641266509675438), 'eval_cosine_accuracy_threshold': np.float32(0.72207844), 'eval_cosine_f1': np.float64(0.9641775403529848), 'eval_cosine_f1_threshold': np.float32(0.72207844), 'eval_cosine_precision': 0.9628117103550371, 'eval_cosine_recall': np.float64(0.9655472509470666), 'eval_cosine_ap': np.float64(0.9929222851127353), 'eval_runtime': 85.7283, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.18}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7260119915008545\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7257403135299683\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.726012), 'eval_cosine_f1': np.float64(0.9640912924578935), 'eval_cosine_f1_threshold': np.float32(0.7257403), 'eval_cosine_precision': 0.9626397182667279, 'eval_cosine_recall': np.float64(0.9655472509470666), 'eval_cosine_ap': np.float64(0.9928828244061584), 'eval_runtime': 85.7194, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.19}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7277708649635315\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7277708649635315\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640754581754889), 'eval_cosine_accuracy_threshold': np.float32(0.72777086), 'eval_cosine_f1': np.float64(0.9641025641025641), 'eval_cosine_f1_threshold': np.float32(0.72777086), 'eval_cosine_precision': 0.9633756740869477, 'eval_cosine_recall': np.float64(0.9648305518582984), 'eval_cosine_ap': np.float64(0.9928895072656814), 'eval_runtime': 85.7615, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.2}\n",
      "{'loss': 0.0042, 'grad_norm': 0.06991446763277054, 'learning_rate': 7.996694821887623e-08, 'epoch': 4.2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7213796973228455\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213796973228455\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642162383536398), 'eval_cosine_accuracy_threshold': np.float32(0.7213797), 'eval_cosine_f1': np.float64(0.9642683706070287), 'eval_cosine_f1_threshold': np.float32(0.7213797), 'eval_cosine_precision': 0.962865601551733, 'eval_cosine_recall': np.float64(0.9656752329272038), 'eval_cosine_ap': np.float64(0.9929583963788682), 'eval_runtime': 86.261, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.2}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.723294198513031\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173246145248413\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641010545715163), 'eval_cosine_accuracy_threshold': np.float32(0.7232942), 'eval_cosine_f1': np.float64(0.9642278703786362), 'eval_cosine_f1_threshold': np.float32(0.7173246), 'eval_cosine_precision': 0.9608336510357097, 'eval_cosine_recall': np.float64(0.9676461554213167), 'eval_cosine_ap': np.float64(0.9929467549261732), 'eval_runtime': 85.8315, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.21}\n",
      "{'loss': 0.0043, 'grad_norm': 0.07293850183486938, 'learning_rate': 7.767168564083731e-08, 'epoch': 4.22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237836122512817\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7232121229171753\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640882563735026), 'eval_cosine_accuracy_threshold': np.float32(0.7237836), 'eval_cosine_f1': np.float64(0.9641543178334184), 'eval_cosine_f1_threshold': np.float32(0.7232121), 'eval_cosine_precision': 0.9623839640926247, 'eval_cosine_recall': np.float64(0.9659311968874782), 'eval_cosine_ap': np.float64(0.9929152825140464), 'eval_runtime': 85.8151, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.22}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7243829965591431\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205039858818054\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640626599774752), 'eval_cosine_accuracy_threshold': np.float32(0.724383), 'eval_cosine_f1': np.float64(0.9641576603590614), 'eval_cosine_f1_threshold': np.float32(0.720504), 'eval_cosine_precision': 0.9612752207210645, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9929037945690686), 'eval_runtime': 85.9597, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.23}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7276598215103149\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7261604070663452\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7276598), 'eval_cosine_f1': np.float64(0.9640142563329545), 'eval_cosine_f1_threshold': np.float32(0.7261604), 'eval_cosine_precision': 0.9622319128860327, 'eval_cosine_recall': np.float64(0.965803214907341), 'eval_cosine_ap': np.float64(0.9928646176596796), 'eval_runtime': 85.8483, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.24}\n",
      "{'loss': 0.0044, 'grad_norm': 0.04924662038683891, 'learning_rate': 7.537642306279839e-08, 'epoch': 4.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7275127172470093\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237931489944458\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.7275127), 'eval_cosine_f1': np.float64(0.9640181249601124), 'eval_cosine_f1_threshold': np.float32(0.72379315), 'eval_cosine_precision': 0.96145326781577, 'eval_cosine_recall': np.float64(0.9665967031841917), 'eval_cosine_ap': np.float64(0.9928794085436049), 'eval_runtime': 85.9889, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.25}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7219719886779785\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7173488140106201\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641266509675438), 'eval_cosine_accuracy_threshold': np.float32(0.721972), 'eval_cosine_f1': np.float64(0.9642383430262217), 'eval_cosine_f1_threshold': np.float32(0.7173488), 'eval_cosine_precision': 0.9609049313675648, 'eval_cosine_recall': np.float64(0.9675949626292618), 'eval_cosine_ap': np.float64(0.9929250028891784), 'eval_runtime': 86.3762, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.26}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1332903951406479, 'learning_rate': 7.308116048475946e-08, 'epoch': 4.27}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216030359268188\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7216030359268188\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640754581754889), 'eval_cosine_accuracy_threshold': np.float32(0.72160304), 'eval_cosine_f1': np.float64(0.964145665419152), 'eval_cosine_f1_threshold': np.float32(0.72160304), 'eval_cosine_precision': 0.962265113077178, 'eval_cosine_recall': np.float64(0.966033582471588), 'eval_cosine_ap': np.float64(0.9929187809859428), 'eval_runtime': 85.8721, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.27}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7248942852020264\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7230901122093201\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963934677997338), 'eval_cosine_accuracy_threshold': np.float32(0.7248943), 'eval_cosine_f1': np.float64(0.964012612819629), 'eval_cosine_f1_threshold': np.float32(0.7230901), 'eval_cosine_precision': 0.9615942951738189, 'eval_cosine_recall': np.float64(0.9664431248080271), 'eval_cosine_ap': np.float64(0.9928876146314591), 'eval_runtime': 85.8185, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.28}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7205000519752502\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7205000519752502\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640882563735026), 'eval_cosine_accuracy_threshold': np.float32(0.72050005), 'eval_cosine_f1': np.float64(0.9641552336424721), 'eval_cosine_f1_threshold': np.float32(0.72050005), 'eval_cosine_precision': 0.9623603814964043, 'eval_cosine_recall': np.float64(0.9659567932835057), 'eval_cosine_ap': np.float64(0.9929280464715805), 'eval_runtime': 85.8076, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.29}\n",
      "{'loss': 0.0043, 'grad_norm': 0.08653116226196289, 'learning_rate': 7.078589790672053e-08, 'epoch': 4.29}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220516204833984\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7219510078430176\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640114671854203), 'eval_cosine_accuracy_threshold': np.float32(0.7220516), 'eval_cosine_f1': np.float64(0.9640666530361889), 'eval_cosine_f1_threshold': np.float32(0.721951), 'eval_cosine_precision': 0.9625905889558029, 'eval_cosine_recall': np.float64(0.9655472509470666), 'eval_cosine_ap': np.float64(0.9928958687058038), 'eval_runtime': 85.8453, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7246244549751282\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7246244549751282\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639474761953517), 'eval_cosine_accuracy_threshold': np.float32(0.72462445), 'eval_cosine_f1': np.float64(0.9639875739871906), 'eval_cosine_f1_threshold': np.float32(0.72462445), 'eval_cosine_precision': 0.9629166134593283, 'eval_cosine_recall': np.float64(0.9650609194225453), 'eval_cosine_ap': np.float64(0.9928805613213796), 'eval_runtime': 85.855, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.31}\n",
      "{'loss': 0.0044, 'grad_norm': 0.0660473182797432, 'learning_rate': 6.84906353286816e-08, 'epoch': 4.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.718635618686676\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7157408595085144\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.7186356), 'eval_cosine_f1': np.float64(0.9641476946623303), 'eval_cosine_f1_threshold': np.float32(0.71574086), 'eval_cosine_precision': 0.9608511071001856, 'eval_cosine_recall': np.float64(0.9674669806491246), 'eval_cosine_ap': np.float64(0.9929241805656639), 'eval_runtime': 86.3649, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.31}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7259732484817505\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145121097564697\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.72597325), 'eval_cosine_f1': np.float64(0.964036103932477), 'eval_cosine_f1_threshold': np.float32(0.7145121), 'eval_cosine_precision': 0.9589696831547755, 'eval_cosine_recall': np.float64(0.9691563427869356), 'eval_cosine_ap': np.float64(0.992890429350069), 'eval_runtime': 85.9042, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.32}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7107157707214355\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7107157707214355\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7107158), 'eval_cosine_f1': np.float64(0.9641511355535187), 'eval_cosine_f1_threshold': np.float32(0.7107158), 'eval_cosine_precision': 0.9590720291763752, 'eval_cosine_recall': np.float64(0.9692843247670728), 'eval_cosine_ap': np.float64(0.9929219318219976), 'eval_runtime': 85.9131, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.33}\n",
      "{'loss': 0.0043, 'grad_norm': 0.07468266040086746, 'learning_rate': 6.619537275064267e-08, 'epoch': 4.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7161709070205688\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7161709070205688\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638066960172008), 'eval_cosine_accuracy_threshold': np.float32(0.7161709), 'eval_cosine_f1': np.float64(0.9639653414882773), 'eval_cosine_f1_threshold': np.float32(0.7161709), 'eval_cosine_precision': 0.9597584492032883, 'eval_cosine_recall': np.float64(0.9682092761339204), 'eval_cosine_ap': np.float64(0.9928706081841756), 'eval_runtime': 85.861, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.34}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7133073806762695\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7133073806762695\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.7133074), 'eval_cosine_f1': np.float64(0.9640919856367942), 'eval_cosine_f1_threshold': np.float32(0.7133074), 'eval_cosine_precision': 0.9592307302488218, 'eval_cosine_recall': np.float64(0.9690027644107709), 'eval_cosine_ap': np.float64(0.992896456430103), 'eval_runtime': 85.9499, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.35}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1540537327528, 'learning_rate': 6.390011017260374e-08, 'epoch': 4.36}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7178636193275452\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7160145044326782\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637299068291184), 'eval_cosine_accuracy_threshold': np.float32(0.7178636), 'eval_cosine_f1': np.float64(0.9639183133020982), 'eval_cosine_f1_threshold': np.float32(0.7160145), 'eval_cosine_precision': 0.9589370756915594, 'eval_cosine_recall': np.float64(0.968951571618716), 'eval_cosine_ap': np.float64(0.992854444415435), 'eval_runtime': 85.9569, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.36}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7266000509262085\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7195454835891724\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9636915122350773), 'eval_cosine_accuracy_threshold': np.float32(0.72660005), 'eval_cosine_f1': np.float64(0.96380050167437), 'eval_cosine_f1_threshold': np.float32(0.7195455), 'eval_cosine_precision': 0.9589044566621906, 'eval_cosine_recall': np.float64(0.9687468004504965), 'eval_cosine_ap': np.float64(0.9928286311023098), 'eval_runtime': 86.3902, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.37}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7290514707565308\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.713769793510437\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637810996211733), 'eval_cosine_accuracy_threshold': np.float32(0.7290515), 'eval_cosine_f1': np.float64(0.9639758821583942), 'eval_cosine_f1_threshold': np.float32(0.7137698), 'eval_cosine_precision': 0.9581500025287008, 'eval_cosine_recall': np.float64(0.9698730418757039), 'eval_cosine_ap': np.float64(0.992876034036325), 'eval_runtime': 85.928, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.38}\n",
      "{'loss': 0.0044, 'grad_norm': 0.05946063995361328, 'learning_rate': 6.160484759456481e-08, 'epoch': 4.38}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234205007553101\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7145962715148926\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638578888092556), 'eval_cosine_accuracy_threshold': np.float32(0.7234205), 'eval_cosine_f1': np.float64(0.9640274949083504), 'eval_cosine_f1_threshold': np.float32(0.7145963), 'eval_cosine_precision': 0.9588524258077585, 'eval_cosine_recall': np.float64(0.9692587283710453), 'eval_cosine_ap': np.float64(0.9928891694822318), 'eval_runtime': 85.8517, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.39}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7257205247879028\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7178703546524048\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638834852052831), 'eval_cosine_accuracy_threshold': np.float32(0.7257205), 'eval_cosine_f1': np.float64(0.9640263942319941), 'eval_cosine_f1_threshold': np.float32(0.71787035), 'eval_cosine_precision': 0.9595526702845261, 'eval_cosine_recall': np.float64(0.9685420292822771), 'eval_cosine_ap': np.float64(0.9928937958600196), 'eval_runtime': 85.8918, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.4}\n",
      "{'loss': 0.0042, 'grad_norm': 0.08199623227119446, 'learning_rate': 5.930958501652589e-08, 'epoch': 4.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237780094146729\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7189065217971802\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641266509675438), 'eval_cosine_accuracy_threshold': np.float32(0.723778), 'eval_cosine_f1': np.float64(0.9642191766837659), 'eval_cosine_f1_threshold': np.float32(0.7189065), 'eval_cosine_precision': 0.9613975265916841, 'eval_cosine_recall': np.float64(0.9670574383126855), 'eval_cosine_ap': np.float64(0.9929378783481544), 'eval_runtime': 85.8698, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.41}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7267299294471741\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7267299294471741\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963934677997338), 'eval_cosine_accuracy_threshold': np.float32(0.7267299), 'eval_cosine_f1': np.float64(0.9639605074687948), 'eval_cosine_f1_threshold': np.float32(0.7267299), 'eval_cosine_precision': 0.9632706267252837, 'eval_cosine_recall': np.float64(0.9646513770861063), 'eval_cosine_ap': np.float64(0.9928867437814178), 'eval_runtime': 85.8313, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.42}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7274101376533508\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7220350503921509\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639474761953517), 'eval_cosine_accuracy_threshold': np.float32(0.72741014), 'eval_cosine_f1': np.float64(0.9639707477697089), 'eval_cosine_f1_threshold': np.float32(0.72203505), 'eval_cosine_precision': 0.961308387425226, 'eval_cosine_recall': np.float64(0.9666478959762466), 'eval_cosine_ap': np.float64(0.9928928239905914), 'eval_runtime': 86.3611, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.43}\n",
      "{'loss': 0.0043, 'grad_norm': 0.18739734590053558, 'learning_rate': 5.701432243848696e-08, 'epoch': 4.43}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7301583290100098\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7206141352653503\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638706870072694), 'eval_cosine_accuracy_threshold': np.float32(0.7301583), 'eval_cosine_f1': np.float64(0.9639266555384243), 'eval_cosine_f1_threshold': np.float32(0.72061414), 'eval_cosine_precision': 0.9597320545025501, 'eval_cosine_recall': np.float64(0.9681580833418655), 'eval_cosine_ap': np.float64(0.9928766365120303), 'eval_runtime': 85.9108, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.43}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7213287353515625\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213287353515625\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.72132874), 'eval_cosine_f1': np.float64(0.963992032279483), 'eval_cosine_f1_threshold': np.float32(0.72132874), 'eval_cosine_precision': 0.9617814920505503, 'eval_cosine_recall': np.float64(0.9662127572437801), 'eval_cosine_ap': np.float64(0.9929088224409011), 'eval_runtime': 85.8574, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.44}\n",
      "{'loss': 0.0043, 'grad_norm': 0.0458865761756897, 'learning_rate': 5.471905986044803e-08, 'epoch': 4.45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7229738235473633\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7226717472076416\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.964024265383434), 'eval_cosine_accuracy_threshold': np.float32(0.7229738), 'eval_cosine_f1': np.float64(0.964073463440819), 'eval_cosine_f1_threshold': np.float32(0.72267175), 'eval_cosine_precision': 0.9627568602425016, 'eval_cosine_recall': np.float64(0.965393672570902), 'eval_cosine_ap': np.float64(0.9929249046616476), 'eval_runtime': 86.059, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.45}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7247008681297302\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7155144810676575\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.72470087), 'eval_cosine_f1': np.float64(0.9640254777070064), 'eval_cosine_f1_threshold': np.float32(0.7155145), 'eval_cosine_precision': 0.9595759789003855, 'eval_cosine_recall': np.float64(0.9685164328862497), 'eval_cosine_ap': np.float64(0.9929139383452656), 'eval_runtime': 85.9192, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.46}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722728431224823\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.722728431224823\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642418347496673), 'eval_cosine_accuracy_threshold': np.float32(0.72272843), 'eval_cosine_f1': np.float64(0.9642738408817737), 'eval_cosine_f1_threshold': np.float32(0.72272843), 'eval_cosine_precision': 0.9634115182175891, 'eval_cosine_recall': np.float64(0.9651377086106276), 'eval_cosine_ap': np.float64(0.9929477161598906), 'eval_runtime': 85.9361, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.47}\n",
      "{'loss': 0.0043, 'grad_norm': 0.08458975702524185, 'learning_rate': 5.24237972824091e-08, 'epoch': 4.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7208667397499084\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7163518667221069\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642290365516535), 'eval_cosine_accuracy_threshold': np.float32(0.72086674), 'eval_cosine_f1': np.float64(0.964265659298586), 'eval_cosine_f1_threshold': np.float32(0.71635187), 'eval_cosine_precision': 0.9615658725310527, 'eval_cosine_recall': np.float64(0.9669806491246032), 'eval_cosine_ap': np.float64(0.992954444865032), 'eval_runtime': 86.4122, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.48}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7189314365386963\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7144829630851746\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640626599774752), 'eval_cosine_accuracy_threshold': np.float32(0.71893144), 'eval_cosine_f1': np.float64(0.9641459653058363), 'eval_cosine_f1_threshold': np.float32(0.71448296), 'eval_cosine_precision': 0.9602173195562212, 'eval_cosine_recall': np.float64(0.9681068905498106), 'eval_cosine_ap': np.float64(0.9929209166844074), 'eval_runtime': 85.9125, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.49}\n",
      "{'loss': 0.0044, 'grad_norm': 0.14373913407325745, 'learning_rate': 5.012853470437018e-08, 'epoch': 4.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7209312915802002\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7152698040008545\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639474761953517), 'eval_cosine_accuracy_threshold': np.float32(0.7209313), 'eval_cosine_f1': np.float64(0.9640938144592427), 'eval_cosine_f1_threshold': np.float32(0.7152698), 'eval_cosine_precision': 0.9591841905244489, 'eval_cosine_recall': np.float64(0.9690539572028258), 'eval_cosine_ap': np.float64(0.9928994653854268), 'eval_runtime': 85.9488, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.5}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222918272018433\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7168781757354736\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963934677997338), 'eval_cosine_accuracy_threshold': np.float32(0.7222918), 'eval_cosine_f1': np.float64(0.9640733052304479), 'eval_cosine_f1_threshold': np.float32(0.7168782), 'eval_cosine_precision': 0.9593693762198059, 'eval_cosine_recall': np.float64(0.9688235896385788), 'eval_cosine_ap': np.float64(0.9928867678503871), 'eval_runtime': 85.8796, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.51}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218705415725708\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.721863865852356\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641394491655575), 'eval_cosine_accuracy_threshold': np.float32(0.72187054), 'eval_cosine_f1': np.float64(0.964211816997471), 'eval_cosine_f1_threshold': np.float32(0.72186387), 'eval_cosine_precision': 0.9622699230102483, 'eval_cosine_recall': np.float64(0.9661615644517252), 'eval_cosine_ap': np.float64(0.9929080728445678), 'eval_runtime': 85.8813, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.52}\n",
      "{'loss': 0.0044, 'grad_norm': 0.09992373734712601, 'learning_rate': 4.7833272126331255e-08, 'epoch': 4.52}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.721114993095398\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7210744619369507\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642674311456947), 'eval_cosine_accuracy_threshold': np.float32(0.721115), 'eval_cosine_f1': np.float64(0.9643349854376374), 'eval_cosine_f1_threshold': np.float32(0.72107446), 'eval_cosine_precision': 0.962515299877601, 'eval_cosine_recall': np.float64(0.9661615644517252), 'eval_cosine_ap': np.float64(0.9929354976985599), 'eval_runtime': 85.855, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7215273380279541\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7215273380279541\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641906419576124), 'eval_cosine_accuracy_threshold': np.float32(0.72152734), 'eval_cosine_f1': np.float64(0.964262906480701), 'eval_cosine_f1_threshold': np.float32(0.72152734), 'eval_cosine_precision': 0.962320909600775, 'eval_cosine_recall': np.float64(0.9662127572437801), 'eval_cosine_ap': np.float64(0.9929265654031401), 'eval_runtime': 86.3779, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.54}\n",
      "{'loss': 0.0043, 'grad_norm': 0.10003670305013657, 'learning_rate': 4.553800954829232e-08, 'epoch': 4.54}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7219085693359375\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7216010093688965\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642290365516535), 'eval_cosine_accuracy_threshold': np.float32(0.72190857), 'eval_cosine_f1': np.float64(0.9643016795453094), 'eval_cosine_f1_threshold': np.float32(0.721601), 'eval_cosine_precision': 0.9623473627858363, 'eval_cosine_recall': np.float64(0.966263950035835), 'eval_cosine_ap': np.float64(0.9929259077448772), 'eval_runtime': 85.8411, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.54}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7232054471969604\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7196980714797974\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641394491655575), 'eval_cosine_accuracy_threshold': np.float32(0.72320545), 'eval_cosine_f1': np.float64(0.9642465508887359), 'eval_cosine_f1_threshold': np.float32(0.7196981), 'eval_cosine_precision': 0.9606941409624473, 'eval_cosine_recall': np.float64(0.9678253301935088), 'eval_cosine_ap': np.float64(0.9929143076002165), 'eval_runtime': 85.8951, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.55}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7201064229011536\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7201064229011536\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641138527695301), 'eval_cosine_accuracy_threshold': np.float32(0.7201064), 'eval_cosine_f1': np.float64(0.9642829847400199), 'eval_cosine_f1_threshold': np.float32(0.7201064), 'eval_cosine_precision': 0.9597596226989198, 'eval_cosine_recall': np.float64(0.9688491860346063), 'eval_cosine_ap': np.float64(0.9928996099813892), 'eval_runtime': 85.8517, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.56}\n",
      "{'loss': 0.0045, 'grad_norm': 0.11582567542791367, 'learning_rate': 4.3242746970253395e-08, 'epoch': 4.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.726691722869873\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7146514654159546\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641778437595987), 'eval_cosine_accuracy_threshold': np.float32(0.7266917), 'eval_cosine_f1': np.float64(0.9642493638676846), 'eval_cosine_f1_threshold': np.float32(0.71465147), 'eval_cosine_precision': 0.9585905089547708, 'eval_cosine_recall': np.float64(0.9699754274598137), 'eval_cosine_ap': np.float64(0.992924775491496), 'eval_runtime': 85.9484, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.57}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7229651808738708\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7165566682815552\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641266509675438), 'eval_cosine_accuracy_threshold': np.float32(0.7229652), 'eval_cosine_f1': np.float64(0.9642456962412143), 'eval_cosine_f1_threshold': np.float32(0.71655667), 'eval_cosine_precision': 0.9593594810986116, 'eval_cosine_recall': np.float64(0.969181939182963), 'eval_cosine_ap': np.float64(0.992926388677911), 'eval_runtime': 85.9038, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.58}\n",
      "{'loss': 0.0043, 'grad_norm': 0.048643697053194046, 'learning_rate': 4.094748439221447e-08, 'epoch': 4.59}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7262332439422607\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7169672250747681\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641138527695301), 'eval_cosine_accuracy_threshold': np.float32(0.72623324), 'eval_cosine_f1': np.float64(0.9641833993302691), 'eval_cosine_f1_threshold': np.float32(0.7169672), 'eval_cosine_precision': 0.9592612297636239, 'eval_cosine_recall': np.float64(0.9691563427869356), 'eval_cosine_ap': np.float64(0.992912122765607), 'eval_runtime': 86.4027, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.59}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7256981730461121\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7233679294586182\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641394491655575), 'eval_cosine_accuracy_threshold': np.float32(0.7256982), 'eval_cosine_f1': np.float64(0.9642191291022858), 'eval_cosine_f1_threshold': np.float32(0.7233679), 'eval_cosine_precision': 0.9620814433515111, 'eval_cosine_recall': np.float64(0.9663663356199447), 'eval_cosine_ap': np.float64(0.992920548809143), 'eval_runtime': 85.871, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.6}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7242316007614136\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7242316007614136\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9642930275417221), 'eval_cosine_accuracy_threshold': np.float32(0.7242316), 'eval_cosine_f1': np.float64(0.9643432252127905), 'eval_cosine_f1_threshold': np.float32(0.7242316), 'eval_cosine_precision': 0.962989432844964, 'eval_cosine_recall': np.float64(0.9657008293232313), 'eval_cosine_ap': np.float64(0.9929378435942172), 'eval_runtime': 85.8532, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.61}\n",
      "{'loss': 0.0042, 'grad_norm': 0.10808281600475311, 'learning_rate': 3.8652221814175535e-08, 'epoch': 4.61}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7272360324859619\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7239015102386475\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641394491655575), 'eval_cosine_accuracy_threshold': np.float32(0.72723603), 'eval_cosine_f1': np.float64(0.9641968217499522), 'eval_cosine_f1_threshold': np.float32(0.7239015), 'eval_cosine_precision': 0.9616314891666878, 'eval_cosine_recall': np.float64(0.9667758779563838), 'eval_cosine_ap': np.float64(0.9929060766243515), 'eval_runtime': 85.8465, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.62}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.723860502243042\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7199239730834961\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640370635814477), 'eval_cosine_accuracy_threshold': np.float32(0.7238605), 'eval_cosine_f1': np.float64(0.9641355050852642), 'eval_cosine_f1_threshold': np.float32(0.719924), 'eval_cosine_precision': 0.960146215159669, 'eval_cosine_recall': np.float64(0.9681580833418655), 'eval_cosine_ap': np.float64(0.992895306036929), 'eval_runtime': 85.8737, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.63}\n",
      "{'loss': 0.0043, 'grad_norm': 0.22400552034378052, 'learning_rate': 3.635695923613661e-08, 'epoch': 4.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234634160995483\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7234634160995483\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640882563735026), 'eval_cosine_accuracy_threshold': np.float32(0.7234634), 'eval_cosine_f1': np.float64(0.9641643891599193), 'eval_cosine_f1_threshold': np.float32(0.7234634), 'eval_cosine_precision': 0.9621246877708111, 'eval_cosine_recall': np.float64(0.9662127572437801), 'eval_cosine_ap': np.float64(0.9928985121851761), 'eval_runtime': 85.8426, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.64}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7237963676452637\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237963676452637\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640114671854203), 'eval_cosine_accuracy_threshold': np.float32(0.72379637), 'eval_cosine_f1': np.float64(0.9640932655719283), 'eval_cosine_f1_threshold': np.float32(0.72379637), 'eval_cosine_precision': 0.9619069459307955, 'eval_cosine_recall': np.float64(0.9662895464318624), 'eval_cosine_ap': np.float64(0.9928845120814502), 'eval_runtime': 86.2705, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.65}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7234890460968018\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7234824895858765\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.72348905), 'eval_cosine_f1': np.float64(0.9640585485477814), 'eval_cosine_f1_threshold': np.float32(0.7234825), 'eval_cosine_precision': 0.9621169632386682, 'eval_cosine_recall': np.float64(0.9660079860755606), 'eval_cosine_ap': np.float64(0.9928862666173079), 'eval_runtime': 85.9073, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.65}\n",
      "{'loss': 0.0041, 'grad_norm': 0.16456247866153717, 'learning_rate': 3.406169665809768e-08, 'epoch': 4.66}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7217708230018616\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7217708230018616\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641138527695301), 'eval_cosine_accuracy_threshold': np.float32(0.7217708), 'eval_cosine_f1': np.float64(0.9641835274882485), 'eval_cosine_f1_threshold': np.float32(0.7217708), 'eval_cosine_precision': 0.9623151453340133, 'eval_cosine_recall': np.float64(0.9660591788676154), 'eval_cosine_ap': np.float64(0.9929086429229899), 'eval_runtime': 85.8692, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.66}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228087782859802\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7228087782859802\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641266509675438), 'eval_cosine_accuracy_threshold': np.float32(0.7228088), 'eval_cosine_f1': np.float64(0.9641949287858466), 'eval_cosine_f1_threshold': np.float32(0.7228088), 'eval_cosine_precision': 0.9623632608307622, 'eval_cosine_recall': np.float64(0.966033582471588), 'eval_cosine_ap': np.float64(0.9929128906377888), 'eval_runtime': 85.9208, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.67}\n",
      "{'loss': 0.0044, 'grad_norm': 0.08493207395076752, 'learning_rate': 3.176643408005876e-08, 'epoch': 4.68}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7257115840911865\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213191986083984\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7257116), 'eval_cosine_f1': np.float64(0.9640351995918889), 'eval_cosine_f1_threshold': np.float32(0.7213192), 'eval_cosine_precision': 0.9606781556606172, 'eval_cosine_recall': np.float64(0.9674157878570697), 'eval_cosine_ap': np.float64(0.9928880393971895), 'eval_runtime': 85.861, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.68}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7232778668403625\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7232778668403625\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640754581754889), 'eval_cosine_accuracy_threshold': np.float32(0.72327787), 'eval_cosine_f1': np.float64(0.9641392526349409), 'eval_cosine_f1_threshold': np.float32(0.72327787), 'eval_cosine_precision': 0.9624301782844901, 'eval_cosine_recall': np.float64(0.9658544076993959), 'eval_cosine_ap': np.float64(0.992906512181524), 'eval_runtime': 86.0321, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.69}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.721864640712738\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.721864640712738\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9641010545715163), 'eval_cosine_accuracy_threshold': np.float32(0.72186464), 'eval_cosine_f1': np.float64(0.9641721270644136), 'eval_cosine_f1_threshold': np.float32(0.72186464), 'eval_cosine_precision': 0.9622670371975627, 'eval_cosine_recall': np.float64(0.9660847752636429), 'eval_cosine_ap': np.float64(0.9929111310973388), 'eval_runtime': 86.3389, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.7}\n",
      "{'loss': 0.0043, 'grad_norm': 0.09728089720010757, 'learning_rate': 2.947117150201983e-08, 'epoch': 4.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216974496841431\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7216974496841431\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640626599774752), 'eval_cosine_accuracy_threshold': np.float32(0.72169745), 'eval_cosine_f1': np.float64(0.9641360989067129), 'eval_cosine_f1_threshold': np.float32(0.72169745), 'eval_cosine_precision': 0.9621698786581013, 'eval_cosine_recall': np.float64(0.9661103716596703), 'eval_cosine_ap': np.float64(0.9929035340562179), 'eval_runtime': 85.923, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.71}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7226661443710327\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7174500226974487\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.72266614), 'eval_cosine_f1': np.float64(0.9640018343014369), 'eval_cosine_f1_threshold': np.float32(0.71745), 'eval_cosine_precision': 0.9595040064915306, 'eval_cosine_recall': np.float64(0.9685420292822771), 'eval_cosine_ap': np.float64(0.9928801477261557), 'eval_runtime': 85.8639, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.72}\n",
      "{'loss': 0.0043, 'grad_norm': 0.08474774658679962, 'learning_rate': 2.7175908923980903e-08, 'epoch': 4.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7191852927207947\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7181848287582397\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9637299068291184), 'eval_cosine_accuracy_threshold': np.float32(0.7191853), 'eval_cosine_f1': np.float64(0.9638980891719746), 'eval_cosine_f1_threshold': np.float32(0.7181848), 'eval_cosine_precision': 0.9594491783323189, 'eval_cosine_recall': np.float64(0.9683884509061125), 'eval_cosine_ap': np.float64(0.9928542837539516), 'eval_runtime': 85.8751, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.73}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.723786473274231\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7237405776977539\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638578888092556), 'eval_cosine_accuracy_threshold': np.float32(0.7237865), 'eval_cosine_f1': np.float64(0.9639234523109942), 'eval_cosine_f1_threshold': np.float32(0.7237406), 'eval_cosine_precision': 0.9621780158122928, 'eval_cosine_recall': np.float64(0.9656752329272038), 'eval_cosine_ap': np.float64(0.9928684588100859), 'eval_runtime': 85.8653, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.74}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7233033180236816\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7177703380584717\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638578888092556), 'eval_cosine_accuracy_threshold': np.float32(0.7233033), 'eval_cosine_f1': np.float64(0.9639607493309544), 'eval_cosine_f1_threshold': np.float32(0.71777034), 'eval_cosine_precision': 0.9598751332419674, 'eval_cosine_recall': np.float64(0.9680812941537832), 'eval_cosine_ap': np.float64(0.9928675157243464), 'eval_runtime': 86.3329, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.75}\n",
      "{'loss': 0.0045, 'grad_norm': 0.1423426866531372, 'learning_rate': 2.4880646345941976e-08, 'epoch': 4.75}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7236995697021484\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7236435413360596\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.72369957), 'eval_cosine_f1': np.float64(0.9640164583812517), 'eval_cosine_f1_threshold': np.float32(0.72364354), 'eval_cosine_precision': 0.9625159479459046, 'eval_cosine_recall': np.float64(0.9655216545510392), 'eval_cosine_ap': np.float64(0.9928760446762844), 'eval_runtime': 85.9194, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.76}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722662091255188\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.722662091255188\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639218797993243), 'eval_cosine_accuracy_threshold': np.float32(0.7226621), 'eval_cosine_f1': np.float64(0.9639942268146577), 'eval_cosine_f1_threshold': np.float32(0.7226621), 'eval_cosine_precision': 0.9620650095602294, 'eval_cosine_recall': np.float64(0.9659311968874782), 'eval_cosine_ap': np.float64(0.9928713799173383), 'eval_runtime': 85.8707, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.76}\n",
      "{'loss': 0.0044, 'grad_norm': 0.1216704398393631, 'learning_rate': 2.2585383767903046e-08, 'epoch': 4.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7221157550811768\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7214479446411133\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.72211576), 'eval_cosine_f1': np.float64(0.9640548757121324), 'eval_cosine_f1_threshold': np.float32(0.72144794), 'eval_cosine_precision': 0.9622112295374573, 'eval_cosine_recall': np.float64(0.9659056004914508), 'eval_cosine_ap': np.float64(0.9928946144954345), 'eval_runtime': 85.9644, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.77}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218051552772522\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7134209871292114\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.72180516), 'eval_cosine_f1': np.float64(0.9640774968812852), 'eval_cosine_f1_threshold': np.float32(0.713421), 'eval_cosine_precision': 0.9589263104583439, 'eval_cosine_recall': np.float64(0.9692843247670728), 'eval_cosine_ap': np.float64(0.9928934323231431), 'eval_runtime': 85.8385, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.78}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7208807468414307\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7177738547325134\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638194942152145), 'eval_cosine_accuracy_threshold': np.float32(0.72088075), 'eval_cosine_f1': np.float64(0.9639739521606708), 'eval_cosine_f1_threshold': np.float32(0.71777385), 'eval_cosine_precision': 0.9598761515620639, 'eval_cosine_recall': np.float64(0.9681068905498106), 'eval_cosine_ap': np.float64(0.9928737146977387), 'eval_runtime': 86.0148, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.79}\n",
      "{'loss': 0.0045, 'grad_norm': 0.14671744406223297, 'learning_rate': 2.0290121189864117e-08, 'epoch': 4.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7169171571731567\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7169171571731567\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9638578888092556), 'eval_cosine_accuracy_threshold': np.float32(0.71691716), 'eval_cosine_f1': np.float64(0.9640089722675368), 'eval_cosine_f1_threshold': np.float32(0.71691716), 'eval_cosine_precision': 0.959995938673977, 'eval_cosine_recall': np.float64(0.9680556977577557), 'eval_cosine_ap': np.float64(0.9928850881895209), 'eval_runtime': 86.3575, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.8}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7228683829307556\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.713555097579956\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639218797993243), 'eval_cosine_accuracy_threshold': np.float32(0.7228684), 'eval_cosine_f1': np.float64(0.9640774968812852), 'eval_cosine_f1_threshold': np.float32(0.7135551), 'eval_cosine_precision': 0.9589263104583439, 'eval_cosine_recall': np.float64(0.9692843247670728), 'eval_cosine_ap': np.float64(0.9928936329983871), 'eval_runtime': 85.9361, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.81}\n",
      "{'loss': 0.0043, 'grad_norm': 0.11963985860347748, 'learning_rate': 1.7994858611825193e-08, 'epoch': 4.82}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7219527959823608\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7134003639221191\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639602743933654), 'eval_cosine_accuracy_threshold': np.float32(0.7219528), 'eval_cosine_f1': np.float64(0.9640388507707779), 'eval_cosine_f1_threshold': np.float32(0.71340036), 'eval_cosine_precision': 0.9588999468206336, 'eval_cosine_recall': np.float64(0.9692331319750179), 'eval_cosine_ap': np.float64(0.9928939130623733), 'eval_runtime': 85.9035, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.82}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7226266860961914\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7137198448181152\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.963934677997338), 'eval_cosine_accuracy_threshold': np.float32(0.7226267), 'eval_cosine_f1': np.float64(0.9640538682823756), 'eval_cosine_f1_threshold': np.float32(0.71371984), 'eval_cosine_precision': 0.9588545095457538, 'eval_cosine_recall': np.float64(0.9693099211631002), 'eval_cosine_ap': np.float64(0.9928927719363845), 'eval_runtime': 85.8388, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.83}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722905695438385\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7134881019592285\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.7229057), 'eval_cosine_f1': np.float64(0.9640765823107083), 'eval_cosine_f1_threshold': np.float32(0.7134881), 'eval_cosine_precision': 0.9589495542949756, 'eval_cosine_recall': np.float64(0.9692587283710453), 'eval_cosine_ap': np.float64(0.9929017251043919), 'eval_runtime': 85.9041, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.84}\n",
      "{'loss': 0.0043, 'grad_norm': 0.08813750743865967, 'learning_rate': 1.5699596033786263e-08, 'epoch': 4.84}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216869592666626\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7154279947280884\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.72168696), 'eval_cosine_f1': np.float64(0.9640823087214119), 'eval_cosine_f1_threshold': np.float32(0.715428), 'eval_cosine_precision': 0.9598142933252151, 'eval_cosine_recall': np.float64(0.9683884509061125), 'eval_cosine_ap': np.float64(0.9929017918771982), 'eval_runtime': 85.9018, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.85}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220970392227173\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7175028324127197\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.72209704), 'eval_cosine_f1': np.float64(0.9640662059116064), 'eval_cosine_f1_threshold': np.float32(0.71750283), 'eval_cosine_precision': 0.9605630939675763, 'eval_cosine_recall': np.float64(0.9675949626292618), 'eval_cosine_ap': np.float64(0.9929024995989745), 'eval_runtime': 86.4238, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.86}\n",
      "{'loss': 0.0043, 'grad_norm': 0.13544556498527527, 'learning_rate': 1.3404333455747337e-08, 'epoch': 4.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7221671342849731\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7221671342849731\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640498617794615), 'eval_cosine_accuracy_threshold': np.float32(0.72216713), 'eval_cosine_f1': np.float64(0.9641072820434188), 'eval_cosine_f1_threshold': np.float32(0.72216713), 'eval_cosine_precision': 0.9625698466562906, 'eval_cosine_recall': np.float64(0.9656496365311764), 'eval_cosine_ap': np.float64(0.9929040919173457), 'eval_runtime': 85.7894, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7218401432037354\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7218401432037354\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640114671854203), 'eval_cosine_accuracy_threshold': np.float32(0.72184014), 'eval_cosine_f1': np.float64(0.9640749163195952), 'eval_cosine_f1_threshold': np.float32(0.72184014), 'eval_cosine_precision': 0.962378207417232, 'eval_cosine_recall': np.float64(0.9657776185113136), 'eval_cosine_ap': np.float64(0.9929038248415961), 'eval_runtime': 85.8162, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.87}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.72169429063797\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.721634030342102\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9640114671854203), 'eval_cosine_accuracy_threshold': np.float32(0.7216943), 'eval_cosine_f1': np.float64(0.9640776699029125), 'eval_cosine_f1_threshold': np.float32(0.72163403), 'eval_cosine_precision': 0.9623074569009487, 'eval_cosine_recall': np.float64(0.9658544076993959), 'eval_cosine_ap': np.float64(0.9929028780010948), 'eval_runtime': 85.8434, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.88}\n",
      "{'loss': 0.0042, 'grad_norm': 0.11610807478427887, 'learning_rate': 1.1109070877708409e-08, 'epoch': 4.89}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7216083407402039\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7216083407402039\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.964024265383434), 'eval_cosine_accuracy_threshold': np.float32(0.72160834), 'eval_cosine_f1': np.float64(0.964087233145529), 'eval_cosine_f1_threshold': np.float32(0.72160834), 'eval_cosine_precision': 0.9624027547506696, 'eval_cosine_recall': np.float64(0.9657776185113136), 'eval_cosine_ap': np.float64(0.9929043737167373), 'eval_runtime': 85.8897, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.89}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.722754716873169\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7130337953567505\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.7227547), 'eval_cosine_f1': np.float64(0.9640633950735154), 'eval_cosine_f1_threshold': np.float32(0.7130338), 'eval_cosine_precision': 0.9589485147010408, 'eval_cosine_recall': np.float64(0.9692331319750179), 'eval_cosine_ap': np.float64(0.9929042440909361), 'eval_runtime': 85.8657, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.9}\n",
      "{'loss': 0.0042, 'grad_norm': 0.100834921002388, 'learning_rate': 8.813808299669482e-09, 'epoch': 4.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7212318181991577\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7206143736839294\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.964024265383434), 'eval_cosine_accuracy_threshold': np.float32(0.7212318), 'eval_cosine_f1': np.float64(0.9640945726730448), 'eval_cosine_f1_threshold': np.float32(0.7206144), 'eval_cosine_precision': 0.9622141199867418, 'eval_cosine_recall': np.float64(0.9659823896795331), 'eval_cosine_ap': np.float64(0.9929082873983377), 'eval_runtime': 86.479, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.91}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7213010191917419\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7213010191917419\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639858707893929), 'eval_cosine_accuracy_threshold': np.float32(0.721301), 'eval_cosine_f1': np.float64(0.9640512021257568), 'eval_cosine_f1_threshold': np.float32(0.721301), 'eval_cosine_precision': 0.9623055343024739, 'eval_cosine_recall': np.float64(0.965803214907341), 'eval_cosine_ap': np.float64(0.9929016517265671), 'eval_runtime': 85.8781, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.92}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7210965752601624\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.721034049987793\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639986689874066), 'eval_cosine_accuracy_threshold': np.float32(0.7210966), 'eval_cosine_f1': np.float64(0.9640662723707575), 'eval_cosine_f1_threshold': np.float32(0.72103405), 'eval_cosine_precision': 0.9622593395384419, 'eval_cosine_recall': np.float64(0.9658800040954234), 'eval_cosine_ap': np.float64(0.9929027558176918), 'eval_runtime': 85.8984, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.93}\n",
      "{'loss': 0.0044, 'grad_norm': 0.0932895839214325, 'learning_rate': 6.518545721630554e-09, 'epoch': 4.93}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7219943404197693\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7156883478164673\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639474761953517), 'eval_cosine_accuracy_threshold': np.float32(0.72199434), 'eval_cosine_f1': np.float64(0.9640340930807354), 'eval_cosine_f1_threshold': np.float32(0.71568835), 'eval_cosine_precision': 0.959693579889912, 'eval_cosine_recall': np.float64(0.9684140473021399), 'eval_cosine_ap': np.float64(0.9928943922215507), 'eval_runtime': 85.9283, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.94}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7219054698944092\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7159256935119629\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639218797993243), 'eval_cosine_accuracy_threshold': np.float32(0.72190547), 'eval_cosine_f1': np.float64(0.9640141141102889), 'eval_cosine_f1_threshold': np.float32(0.7159257), 'eval_cosine_precision': 0.9595283377710156, 'eval_cosine_recall': np.float64(0.9685420292822771), 'eval_cosine_ap': np.float64(0.9928867108357184), 'eval_runtime': 85.9081, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.95}\n",
      "{'loss': 0.0043, 'grad_norm': 0.11748214811086655, 'learning_rate': 4.223283143591627e-09, 'epoch': 4.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7220149636268616\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7160997986793518\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.72201496), 'eval_cosine_f1': np.float64(0.9640000000000001), 'eval_cosine_f1_threshold': np.float32(0.7160998), 'eval_cosine_precision': 0.9595506187867722, 'eval_cosine_recall': np.float64(0.9684908364902222), 'eval_cosine_ap': np.float64(0.9928858085268422), 'eval_runtime': 85.9112, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.96}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7221924066543579\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7163410782814026\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639218797993243), 'eval_cosine_accuracy_threshold': np.float32(0.7221924), 'eval_cosine_f1': np.float64(0.9639990827791791), 'eval_cosine_f1_threshold': np.float32(0.7163411), 'eval_cosine_precision': 0.9595739284808521, 'eval_cosine_recall': np.float64(0.9684652400941948), 'eval_cosine_ap': np.float64(0.992883881139454), 'eval_runtime': 86.3463, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.97}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222610712051392\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7163758873939514\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.7222611), 'eval_cosine_f1': np.float64(0.9640000000000001), 'eval_cosine_f1_threshold': np.float32(0.7163759), 'eval_cosine_precision': 0.9595506187867722, 'eval_cosine_recall': np.float64(0.9684908364902222), 'eval_cosine_ap': np.float64(0.9928846314350817), 'eval_runtime': 85.8824, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.98}\n",
      "{'loss': 0.0042, 'grad_norm': 0.07673928886651993, 'learning_rate': 1.928020565552699e-09, 'epoch': 4.98}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7221556901931763\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.7163494825363159\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639218797993243), 'eval_cosine_accuracy_threshold': np.float32(0.7221557), 'eval_cosine_f1': np.float64(0.9640000000000001), 'eval_cosine_f1_threshold': np.float32(0.7163495), 'eval_cosine_precision': 0.9595506187867722, 'eval_cosine_recall': np.float64(0.9684908364902222), 'eval_cosine_ap': np.float64(0.9928848429900536), 'eval_runtime': 85.8913, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.99}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trainer is attempting to log a value of \"0.7222327589988708\" of type <class 'numpy.float32'> for key \"eval/cosine_accuracy_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
      "Trainer is attempting to log a value of \"0.716351330280304\" of type <class 'numpy.float32'> for key \"eval/cosine_f1_threshold\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_cosine_accuracy': np.float64(0.9639090816013105), 'eval_cosine_accuracy_threshold': np.float32(0.72223276), 'eval_cosine_f1': np.float64(0.9640000000000001), 'eval_cosine_f1_threshold': np.float32(0.71635133), 'eval_cosine_precision': 0.9595506187867722, 'eval_cosine_recall': np.float64(0.9684908364902222), 'eval_cosine_ap': np.float64(0.9928847739289005), 'eval_runtime': 85.9319, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 4.99}\n",
      "{'train_runtime': 104849.1304, 'train_samples_per_second': 33.243, 'train_steps_per_second': 1.039, 'train_loss': 0.004685079804631256, 'epoch': 5.0}\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open(\"tmp/x_train_all.pkl\", \"rb\") as f:\n",
    "    x_train = pickle.load(f)\n",
    "with open(\"tmp/x_dev_all.pkl\", \"rb\") as f:\n",
    "    x_dev = pickle.load(f)\n",
    "from datasets import Dataset\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
    "os.environ[\"HF_HOME\"] = \"/media/4t/2024_12_04/\"\n",
    "import random\n",
    "random.shuffle(x_train)\n",
    "random.shuffle(x_dev)\n",
    "\n",
    "from sentence_transformers import InputExample\n",
    "from sentence_transformers import losses\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sentence_transformers import evaluation\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "model_id='sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
    "model = SentenceTransformer(model_id) #sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n",
    "model=model.to('cuda')\n",
    "train_loss = losses.ContrastiveLoss(model=model)\n",
    "training_ds = []\n",
    "for example in x_train:\n",
    "    training_ds.append(InputExample(texts=[example['sentence1'], \n",
    "                                example['sentence2']], \n",
    "                        label=float(example['label'])))\n",
    "train_loader = DataLoader(training_ds, shuffle=True, batch_size=32)\n",
    "s1s = []\n",
    "s2s = []\n",
    "scores = []\n",
    "for example in x_dev:\n",
    "    s1s.append(example['sentence1'])\n",
    "    s2s.append(example['sentence2'])\n",
    "    scores.append(float(example['label']))\n",
    "evaluator = evaluation.BinaryClassificationEvaluator(s1s, s2s, scores)\n",
    "\n",
    "\n",
    "# #冻结model的部分参数，只训练最后一层\n",
    "# for param in model.parameters():\n",
    "#     param.requires_grad = False\n",
    "# for param in model.lstm.parameters():\n",
    "#     param.requires_grad = True\n",
    "model.fit(\n",
    "    train_objectives=[(train_loader, train_loss)], \n",
    "    evaluator=evaluator,\n",
    "    evaluation_steps=200,\n",
    "    epochs=5, \n",
    "    warmup_steps=0,\n",
    "    output_path=model_id,\n",
    "    weight_decay=0.01,\n",
    "    optimizer_params={'lr': 0.0000005}, #0.00004\n",
    "    save_best_model=True,\n",
    "    show_progress_bar=True,\n",
    ")\n",
    "# 训练截止的参数\n",
    "# {'loss': 0.0015, 'grad_norm': 0.046571265906095505, 'learning_rate': 6.059771381352431e-08, 'epoch': 9.98}\n",
    "# {'eval_cosine_accuracy': np.float64(0.952198730418757), 'eval_cosine_accuracy_threshold': np.float32(0.7516082), 'eval_cosine_f1': np.float64(0.9522482324814299), 'eval_cosine_f1_threshold': np.float32(0.7516082), 'eval_cosine_precision': 0.9512631229405604, 'eval_cosine_recall': np.float64(0.9532353844578684), 'eval_cosine_ap': np.float64(0.9872881488924428), 'eval_runtime': 31.5035, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 9.98}\n",
    "# {'eval_cosine_accuracy': np.float64(0.9521731340227296), 'eval_cosine_accuracy_threshold': np.float32(0.7518253), 'eval_cosine_f1': np.float64(0.9522189973277416), 'eval_cosine_f1_threshold': np.float32(0.7518253), 'eval_cosine_precision': 0.9513067470556676, 'eval_cosine_recall': np.float64(0.9531329988737586), 'eval_cosine_ap': np.float64(0.9872867364231148), 'eval_runtime': 31.5416, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 10.0}\n",
    "# {'train_runtime': 52113.3579, 'train_samples_per_second': 89.177, 'train_steps_per_second': 2.787, 'train_loss': 0.0021380467543890593, 'epoch': 10.0}\n",
    "\n",
    "# {'loss': 0.0022, 'grad_norm': 0.06531787663698196, 'learning_rate': 1.5424164524421592e-08, 'epoch': 9.96}\n",
    "# {'eval_cosine_accuracy': np.float64(0.9609271014641139), 'eval_cosine_accuracy_threshold': np.float32(0.68769825), 'eval_cosine_f1': np.float64(0.9609483782782541), 'eval_cosine_f1_threshold': np.float32(0.6826815), 'eval_cosine_precision': 0.9591717032616734, 'eval_cosine_recall': np.float64(0.9627316473840484), 'eval_cosine_ap': np.float64(0.9915214463332258), 'eval_runtime': 34.0901, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 9.97}\n",
    "# {'eval_cosine_accuracy': np.float64(0.9609271014641139), 'eval_cosine_accuracy_threshold': np.float32(0.6876598), 'eval_cosine_f1': np.float64(0.9609586591036844), 'eval_cosine_f1_threshold': np.float32(0.68279123), 'eval_cosine_precision': 0.9592430116302796, 'eval_cosine_recall': np.float64(0.9626804545919935), 'eval_cosine_ap': np.float64(0.99152179867341), 'eval_runtime': 32.0231, 'eval_samples_per_second': 0.0, 'eval_steps_per_second': 0.0, 'epoch': 9.99}\n",
    "# {'train_runtime': 54037.9631, 'train_samples_per_second': 129.001, 'train_steps_per_second': 2.016, 'train_loss': 0.002529408910829787, 'epoch': 10.0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-12-08 12:42:41.413614: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1733632961.425227   56211 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1733632961.428638   56211 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-12-08 12:42:41.442026: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.0744,  0.0812, -0.0036,  ..., -0.0249, -0.0012, -0.1403],\n",
      "        [ 0.0535, -0.0634, -0.0053,  ...,  0.0817, -0.0168, -0.0523],\n",
      "        [-0.0322, -0.0680, -0.0039,  ...,  0.0597, -0.0931, -0.1485]],\n",
      "       device='cuda:0')\n",
      "204739 204739\n",
      "428 428\n",
      "13666\n"
     ]
    }
   ],
   "source": [
    "# 制作数据集\n",
    "import pickle\n",
    "import json\n",
    "import random\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
    "os.environ[\"HF_HOME\"] = \"/media/4t/2024_12_04/\"\n",
    "\n",
    "model_id='sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
    "with open(\"all-data/dev_en.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "with open(\"all-data/dev_de.pkl\", \"rb\") as f:\n",
    "    test.extend(pickle.load(f))\n",
    "    \n",
    "with open('./GND/dataset/GND-Subjects-all.json') as f:\n",
    "    gnd_jsons=json.load(f)\n",
    "\n",
    "labels=[]\n",
    "for c in gnd_jsons:\n",
    "    item1={}\n",
    "    item1['sentence2']='Classification Name is '+c['Classification Name']+'. Name is '+c['Name']\n",
    "    item1['Code']=c['Code']\n",
    "    labels.append(item1)\n",
    "x_test=[]\n",
    "for t in test:\n",
    "    if type(t['title'])!=str:\n",
    "        t['title']=' '.join(t['title'])\n",
    "        # break\n",
    "    if type(t['abstract'])!=str:\n",
    "        t['abstract']=' '.join(t['abstract'])\n",
    "    item=t['title']+'.'+t['abstract']\n",
    "    x_test.append(item)\n",
    "  \n",
    "from sentence_transformers import SentenceTransformer\n",
    "import torch\n",
    "model = SentenceTransformer(model_id)\n",
    "model.to('cuda')\n",
    "# 把labels的一维张量转换为二维张量\n",
    "batch_size=32\n",
    "texts=[label['sentence2'] for label in labels]\n",
    "texts=[texts[i:i+batch_size] for i in range(0, len(texts), batch_size)]\n",
    "if len(texts)*batch_size<len(labels):\n",
    "    texts.append([label['sentence2'] for label in labels[len(texts)*batch_size:]])\n",
    "y_test=[]\n",
    "for text in texts:\n",
    "    with torch.no_grad():\n",
    "        embeddings = model.encode(text, convert_to_tensor=True)\n",
    "    item=embeddings.cpu()\n",
    "    y_test.append(item)\n",
    "print(embeddings)\n",
    "\n",
    "y_t=[]\n",
    "i=0\n",
    "for y in y_test:\n",
    "    for e in y:\n",
    "        item={}\n",
    "        item['Code']=labels[i]['Code']\n",
    "        item['embedding']=e\n",
    "        item['sentence2']=labels[i]['sentence2']\n",
    "        y_t.append(item)\n",
    "        i+=1\n",
    "print(len(y_t),len(labels)) # 204739 204739\n",
    "x_texts=[x_test[i:i+batch_size] for i in range(0, len(x_test), batch_size)]\n",
    "if len(x_texts)*batch_size<len(x_test):\n",
    "    x_texts.append([x_test[len(x_texts)*batch_size:]])\n",
    "x_t=[]\n",
    "for x in x_texts:\n",
    "    with torch.no_grad():\n",
    "        embeddings = model.encode(x, convert_to_tensor=True)\n",
    "    item=embeddings.cpu()\n",
    "    x_t.append(item)\n",
    "print(len(x_t),len(x_texts)) # 6340 6340\n",
    "print(len(x_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tt=torch.tensor([y['embedding'].tolist() for y in y_t]).to('cuda')\n",
    "import torch.nn.functional as F\n",
    "x_scores=[]\n",
    "for x in x_t:\n",
    "    x=x.to('cuda')\n",
    "    for xx in x:\n",
    "        with torch.no_grad():\n",
    "            cosine_similarity = F.cosine_similarity(xx, y_tt, dim=1)\n",
    "        x_scores.append(cosine_similarity.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0777257427191629\n",
      "0.1902124281327278\n",
      "0.10180634880574518\n"
     ]
    }
   ],
   "source": [
    "x_scores[0].shape\n",
    "# 找出每个x_score中前topk大的索引\n",
    "topk=5\n",
    "x_scores_topk=[]\n",
    "for x_score in x_scores:\n",
    "    x_score=x_score.cpu()\n",
    "    topk_indices = torch.topk(x_score, topk).indices\n",
    "    x_scores_topk.append(topk_indices)\n",
    "\n",
    "with open(\"all-data/dev_en.pkl\", \"rb\") as f:\n",
    "    test = pickle.load(f)\n",
    "with open(\"all-data/dev_de.pkl\", \"rb\") as f:\n",
    "    test.extend(pickle.load(f))\n",
    "codes=[]\n",
    "for t in test:\n",
    "    code=[]\n",
    "    for c in t['dcterms:subject']:\n",
    "        code.append(c['Code'])\n",
    "    codes.append(code)\n",
    "# print(test[0]['dcterms:subject'])\n",
    "pre_ans=[]\n",
    "recalls=[]\n",
    "f1s=[]\n",
    "for i in range(len(x_scores_topk)):\n",
    "    # print(codes[i])\n",
    "    cnt=0.0\n",
    "    for t in x_scores_topk[i]:\n",
    "        target=gnd_jsons[t]['Code']\n",
    "        if target in codes[i]:\n",
    "            cnt+=1\n",
    "    pre_ans.append(cnt/topk)\n",
    "    recalls.append(cnt/len(codes[i]))\n",
    "    f1=2*pre_ans[-1]*recalls[-1]/(pre_ans[-1]+recalls[-1]) if pre_ans[-1]+recalls[-1]!=0 else 0\n",
    "    f1s.append(f1)\n",
    "\n",
    "print(sum(pre_ans)/len(pre_ans)) \n",
    "print(sum(recalls)/len(recalls))\n",
    "print(sum(f1s)/len(f1s))\n",
    "# baseline-1.0\n",
    "# Avg. Precision@k (k = 5, 10, 15, 20): 0.0403,0.02984,0.02522,0.0218\n",
    "# Avg. Recall@k (k = 5, 10, 15, 20): 0.0959,0.1385,0.1715,0.19459\n",
    "# Avg. F1@k (k = 5, 10, 15, 20): 0.0525,0.04638,0.04205,0.03782\n",
    "# baseline-2.0\n",
    "# Avg. Precision@k (k = 5, 10, 15, 20): 0.0553,0.0410,0.0336,0.0290\n",
    "# Avg. Recall@k (k = 5, 10, 15, 20): 0.13367,0.1899,0.2284,0.25880\n",
    "# Avg. F1@k (k = 5, 10, 15, 20): 0.07199,0.0634,0.05584,0.05015\n",
    "# 20\n",
    "# 0.035493194789993965\n",
    "# 0.31906611889302516\n",
    "# 0.06141082275474869\n",
    "#15\n",
    "# 0.04223620664422754\n",
    "# 0.29002035498940604\n",
    "# 0.07032192985010126\n",
    "# 10\n",
    "# 0.053322113273823836\n",
    "# 0.2502604008390371\n",
    "# 0.08276202123050456\n",
    "# 5\n",
    "# 0.0777257427191629\n",
    "# 0.1902124281327278\n",
    "# 0.10180634880574518"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27332 13666\n"
     ]
    }
   ],
   "source": [
    "print(len(x_scores_topk),len(codes))"
   ]
  }
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